gtvp_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Temperature vs Precipitation Project\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from IPython.core.interactiveshell import InteractiveShell\n",
    "#InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import copy\n",
    "\n",
    "from pprint import pprint\n",
    "\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "\n",
    "\n",
    "# First let's import the package\n",
    "# pyplot is the interface we want to use\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# \"matplotlib inline\" creates static images of plot embedded in the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading and cleaning the data set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 85794 entries, 0 to 85793\n",
      "Data columns (total 14 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Longitude  85794 non-null  float64\n",
      " 1   Latitude   85794 non-null  float64\n",
      " 2   Jan        85794 non-null  float64\n",
      " 3   Feb        85794 non-null  float64\n",
      " 4   Mar        85794 non-null  float64\n",
      " 5   Apr        85794 non-null  float64\n",
      " 6   May        85794 non-null  float64\n",
      " 7   Jun        85794 non-null  float64\n",
      " 8   Jul        85794 non-null  float64\n",
      " 9   Aug        85794 non-null  float64\n",
      " 10  Sep        85794 non-null  float64\n",
      " 11  Oct        85794 non-null  float64\n",
      " 12  Nov        85794 non-null  float64\n",
      " 13  Dec        85794 non-null  float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 9.2 MB\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
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       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>71.25</td>\n",
       "      <td>-26.9</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-22.6</td>\n",
       "      <td>-22.4</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-23.7</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.75</td>\n",
       "      <td>-28.7</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>-24.5</td>\n",
       "      <td>-24.4</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-10.9</td>\n",
       "      <td>-13.8</td>\n",
       "      <td>-26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.25</td>\n",
       "      <td>-29.3</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>-25.4</td>\n",
       "      <td>-25.2</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-14.8</td>\n",
       "      <td>-27.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.75</td>\n",
       "      <td>-28.1</td>\n",
       "      <td>-20.8</td>\n",
       "      <td>-24.3</td>\n",
       "      <td>-24.3</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-11.3</td>\n",
       "      <td>-14.4</td>\n",
       "      <td>-27.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.25</td>\n",
       "      <td>-30.1</td>\n",
       "      <td>-23.3</td>\n",
       "      <td>-26.8</td>\n",
       "      <td>-26.7</td>\n",
       "      <td>-9.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>-14.3</td>\n",
       "      <td>-17.5</td>\n",
       "      <td>-30.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Longitude  Latitude   Jan   Feb   Mar   Apr  May  Jun  Jul  Aug  Sep   Oct  \\\n",
       "0    -179.75     71.25 -26.9 -19.0 -22.6 -22.4 -8.0 -0.8  2.6  0.3 -2.9  -9.2   \n",
       "1    -179.75     68.75 -28.7 -21.2 -24.5 -24.4 -8.0  0.0  3.9  0.9 -2.8 -10.9   \n",
       "2    -179.75     68.25 -29.3 -22.0 -25.4 -25.2 -8.7  0.0  4.1  0.9 -3.5 -11.8   \n",
       "3    -179.75     67.75 -28.1 -20.8 -24.3 -24.3 -7.4  2.5  7.2  3.5 -2.1 -11.3   \n",
       "4    -179.75     67.25 -30.1 -23.3 -26.8 -26.7 -9.1  2.2  7.5  3.1 -3.9 -14.3   \n",
       "\n",
       "    Nov   Dec  \n",
       "0 -12.0 -23.7  \n",
       "1 -13.8 -26.8  \n",
       "2 -14.8 -27.8  \n",
       "3 -14.4 -27.3  \n",
       "4 -17.5 -30.4  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's begin with checking the data first\n",
    "# df = pd.read_csv('Traffic_Crashes_-_Crashes.csv')\n",
    "df1900 = pd.read_csv('air_temp.1900', sep='\\s+',  names=[\"Longitude\", \"Latitude\", \"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\",\n",
    "                                                         \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"])\n",
    "df1900.info()\n",
    "df1900.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 85794 entries, 0 to 85793\n",
      "Data columns (total 14 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Longitude  85794 non-null  float64\n",
      " 1   Latitude   85794 non-null  float64\n",
      " 2   Jan        85794 non-null  float64\n",
      " 3   Feb        85794 non-null  float64\n",
      " 4   Mar        85794 non-null  float64\n",
      " 5   Apr        85794 non-null  float64\n",
      " 6   May        85794 non-null  float64\n",
      " 7   Jun        85794 non-null  float64\n",
      " 8   Jul        85794 non-null  float64\n",
      " 9   Aug        85794 non-null  float64\n",
      " 10  Sep        85794 non-null  float64\n",
      " 11  Oct        85794 non-null  float64\n",
      " 12  Nov        85794 non-null  float64\n",
      " 13  Dec        85794 non-null  float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 9.2 MB\n"
     ]
    },
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       "      <td>-179.75</td>\n",
       "      <td>71.25</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>-27.5</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-16.6</td>\n",
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       "      <th>2</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.25</td>\n",
       "      <td>-34.1</td>\n",
       "      <td>-30.9</td>\n",
       "      <td>-31.7</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>-32.9</td>\n",
       "      <td>-29.8</td>\n",
       "      <td>-30.9</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-8.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.2</td>\n",
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       "      <td>-0.1</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>-16.3</td>\n",
       "      <td>-20.7</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.25</td>\n",
       "      <td>-34.9</td>\n",
       "      <td>-32.3</td>\n",
       "      <td>-33.8</td>\n",
       "      <td>-19.2</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>-23.6</td>\n",
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      "text/plain": [
       "   Longitude  Latitude   Jan   Feb   Mar   Apr  May  Jun  Jul  Aug  Sep   Oct  \\\n",
       "0    -179.75     71.25 -31.0 -27.5 -28.0 -16.6 -9.2  0.1  1.7  0.8 -0.8  -7.5   \n",
       "1    -179.75     68.75 -33.4 -30.0 -30.6 -17.6 -8.9  0.7  2.9  1.4 -0.9  -8.9   \n",
       "2    -179.75     68.25 -34.1 -30.9 -31.7 -18.2 -9.5  0.6  3.2  1.5 -1.5  -9.7   \n",
       "3    -179.75     67.75 -32.9 -29.8 -30.9 -17.0 -8.1  3.1  6.2  4.0 -0.1  -9.2   \n",
       "4    -179.75     67.25 -34.9 -32.3 -33.8 -19.2 -9.7  2.7  6.6  3.6 -1.8 -12.2   \n",
       "\n",
       "    Nov   Dec  \n",
       "0 -14.8 -18.8  \n",
       "1 -16.0 -20.5  \n",
       "2 -16.8 -21.4  \n",
       "3 -16.3 -20.7  \n",
       "4 -19.3 -23.6  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1901 = pd.read_csv('air_temp.1901', sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"])\n",
    "df1901.info()\n",
    "df1901.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
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   "outputs": [
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 85794 entries, 0 to 85793\n",
      "Data columns (total 28 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Longitude  85794 non-null  float64\n",
      " 1   Latitude   85794 non-null  float64\n",
      " 2   Jan        85794 non-null  float64\n",
      " 3   Feb        85794 non-null  float64\n",
      " 4   Mar        85794 non-null  float64\n",
      " 5   Apr        85794 non-null  float64\n",
      " 6   May        85794 non-null  float64\n",
      " 7   Jun        85794 non-null  float64\n",
      " 8   Jul        85794 non-null  float64\n",
      " 9   Aug        85794 non-null  float64\n",
      " 10  Sep        85794 non-null  float64\n",
      " 11  Oct        85794 non-null  float64\n",
      " 12  Nov        85794 non-null  float64\n",
      " 13  Dec        85794 non-null  float64\n",
      " 14  Longitude  85794 non-null  float64\n",
      " 15  Latitude   85794 non-null  float64\n",
      " 16  Jan        85794 non-null  float64\n",
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      " 25  Oct        85794 non-null  float64\n",
      " 26  Nov        85794 non-null  float64\n",
      " 27  Dec        85794 non-null  float64\n",
      "dtypes: float64(28)\n",
      "memory usage: 18.3 MB\n"
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       "      <td>-16.0</td>\n",
       "      <td>-20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.25</td>\n",
       "      <td>-29.3</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>-25.4</td>\n",
       "      <td>-25.2</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>-31.7</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>-16.8</td>\n",
       "      <td>-21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.75</td>\n",
       "      <td>-28.1</td>\n",
       "      <td>-20.8</td>\n",
       "      <td>-24.3</td>\n",
       "      <td>-24.3</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-30.9</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-8.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>-16.3</td>\n",
       "      <td>-20.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.25</td>\n",
       "      <td>-30.1</td>\n",
       "      <td>-23.3</td>\n",
       "      <td>-26.8</td>\n",
       "      <td>-26.7</td>\n",
       "      <td>-9.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-33.8</td>\n",
       "      <td>-19.2</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>-23.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Longitude  Latitude   Jan   Feb   Mar   Apr  May  Jun  Jul  Aug  ...   Mar  \\\n",
       "0    -179.75     71.25 -26.9 -19.0 -22.6 -22.4 -8.0 -0.8  2.6  0.3  ... -28.0   \n",
       "1    -179.75     68.75 -28.7 -21.2 -24.5 -24.4 -8.0  0.0  3.9  0.9  ... -30.6   \n",
       "2    -179.75     68.25 -29.3 -22.0 -25.4 -25.2 -8.7  0.0  4.1  0.9  ... -31.7   \n",
       "3    -179.75     67.75 -28.1 -20.8 -24.3 -24.3 -7.4  2.5  7.2  3.5  ... -30.9   \n",
       "4    -179.75     67.25 -30.1 -23.3 -26.8 -26.7 -9.1  2.2  7.5  3.1  ... -33.8   \n",
       "\n",
       "    Apr  May  Jun  Jul  Aug  Sep   Oct   Nov   Dec  \n",
       "0 -16.6 -9.2  0.1  1.7  0.8 -0.8  -7.5 -14.8 -18.8  \n",
       "1 -17.6 -8.9  0.7  2.9  1.4 -0.9  -8.9 -16.0 -20.5  \n",
       "2 -18.2 -9.5  0.6  3.2  1.5 -1.5  -9.7 -16.8 -21.4  \n",
       "3 -17.0 -8.1  3.1  6.2  4.0 -0.1  -9.2 -16.3 -20.7  \n",
       "4 -19.2 -9.7  2.7  6.6  3.6 -1.8 -12.2 -19.3 -23.6  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horizontal_stack = pd.concat([df1900, df1901], axis=1)\n",
    "horizontal_stack.info()\n",
    "horizontal_stack.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finding Mean Monthly Temperature for all locations:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of locations 85794\n",
      "First 10 locations mean of January temperature over these 115 years [-20.96137796198625, -24.45661837395835, -25.131482426337037, -24.033856462913093, -26.06643366000808, -22.15940931684048, -17.85112352212908, -17.9250874739215, -18.968678124794337, 28.941589688074693]\n"
     ]
    }
   ],
   "source": [
    "# Finding the mean temperature of all months over these 115 years\n",
    "# means[0] = mean of all January temperatures for a location\n",
    "# .\n",
    "# .\n",
    "# .\n",
    "# means[11] = mean of all December temperatures for a location\n",
    "\n",
    "\n",
    "# Open each of the 115 files and add headers to them. Then we concatenate them.\n",
    "df = pd.read_csv('air_temp.1900', sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan\", \"Feb\", \"Mar\", \"Apr\", \n",
    "                                                    \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \n",
    "                                                    \"Nov\", \"Dec\"])\n",
    "\n",
    "# We start with finding the mean for the month of January\n",
    "means = []\n",
    "means.append(df[\"Jan\"]) \n",
    "means[0]\n",
    "\n",
    "# This for loop goes from files of year 1901 to 2014\n",
    "for x in range(1901,2015):\n",
    "    yearS = str(x)\n",
    "    tempDf = pd.read_csv('air_temp.'+yearS, sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan\", \"Feb\", \"Mar\", \"Apr\", \n",
    "                                                    \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \n",
    "                                                    \"Nov\", \"Dec\"])\n",
    "    # Appending every January to the list of means and finding its average \n",
    "    neans = []\n",
    "    neans.append(tempDf[\"Jan\"]) \n",
    "    neans[0]\n",
    "    means[0] = [(g + h) / 2 for g, h in zip(means[0], neans[0])]\n",
    "\n",
    "# Let's look at the first 10 locations and their mean of January temperature over these 115 years    \n",
    "print('Number of locations', len(means[0]))\n",
    "print('First 10 locations mean of January temperature over these 115 years', means[0][:10] )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 locations and their mean of February temperature over these 115 years [-20.779783236809827, -24.331992323388192, -25.416651118350963, -24.64637796694462, -27.391093202909516, -23.008203378518125, -17.784674004420168, -16.846543607562822, -17.44647308741829, 28.7546216415445]\n"
     ]
    }
   ],
   "source": [
    "# Now let's find the mean for the month of February\n",
    "means.append(df[\"Feb\"]) \n",
    "\n",
    "# This for loop goes from files of year 1901 to 2014\n",
    "for x in range(1901,2015):\n",
    "    yearS = str(x)\n",
    "    tempDf = pd.read_csv('air_temp.'+yearS, sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan\", \"Feb\", \"Mar\", \"Apr\", \n",
    "                                                    \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \n",
    "                                                    \"Nov\", \"Dec\"])\n",
    "    neans = []\n",
    "    neans.append(tempDf[\"Feb\"]) \n",
    "    neans[0]\n",
    "    means[1] = [(g + h) / 2 for g, h in zip(means[1], neans[0])]\n",
    "\n",
    "print('First 10 locations and their mean of February temperature over these 115 years', means[1][:10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's find the mean for all of the remaining months\n",
    "listMonths = [\"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
    "for m in listMonths:\n",
    "    means.append(df[m]) \n",
    "\n",
    "# This for loop goes from files of year 1901 to 2014\n",
    "for x in range(1901,2015):\n",
    "    yearS = str(x)\n",
    "    tempDf = pd.read_csv('air_temp.'+yearS, sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan\", \"Feb\", \"Mar\", \"Apr\", \n",
    "                                                    \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \n",
    "                                                    \"Nov\", \"Dec\"])\n",
    "    \n",
    "    # Appending every month to the list of means and finding its average \n",
    "    neans = []\n",
    "    for m in listMonths:\n",
    "        neans.append(tempDf[m])\n",
    "\n",
    "    for i in range(2,12):\n",
    "        means[i] = [(g + h) / 2 for g, h in zip(means[i], neans[i-2])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting all these means of months to list of columns\n",
    "tempp = list(zip(means[0],means[1],means[2],means[3],means[4],means[5],means[6],means[7],means[8],means[9]\n",
    "        ,means[10],means[11] ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-20.961378</td>\n",
       "      <td>-20.779783</td>\n",
       "      <td>-19.675764</td>\n",
       "      <td>-14.790577</td>\n",
       "      <td>-5.732376</td>\n",
       "      <td>0.135406</td>\n",
       "      <td>3.152787</td>\n",
       "      <td>3.225932</td>\n",
       "      <td>0.135928</td>\n",
       "      <td>-4.684637</td>\n",
       "      <td>-9.416071</td>\n",
       "      <td>-18.191157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-24.456618</td>\n",
       "      <td>-24.331992</td>\n",
       "      <td>-23.543032</td>\n",
       "      <td>-16.092644</td>\n",
       "      <td>-6.216872</td>\n",
       "      <td>0.726472</td>\n",
       "      <td>3.691958</td>\n",
       "      <td>3.264084</td>\n",
       "      <td>-0.171904</td>\n",
       "      <td>-6.644039</td>\n",
       "      <td>-12.713751</td>\n",
       "      <td>-21.234518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-25.131482</td>\n",
       "      <td>-25.416651</td>\n",
       "      <td>-24.716702</td>\n",
       "      <td>-16.879098</td>\n",
       "      <td>-7.093584</td>\n",
       "      <td>0.708316</td>\n",
       "      <td>3.854926</td>\n",
       "      <td>3.230236</td>\n",
       "      <td>-0.916411</td>\n",
       "      <td>-7.677704</td>\n",
       "      <td>-14.062823</td>\n",
       "      <td>-22.229460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-24.033856</td>\n",
       "      <td>-24.646378</td>\n",
       "      <td>-24.136576</td>\n",
       "      <td>-15.999113</td>\n",
       "      <td>-5.939159</td>\n",
       "      <td>3.164843</td>\n",
       "      <td>6.835533</td>\n",
       "      <td>5.678750</td>\n",
       "      <td>0.305764</td>\n",
       "      <td>-7.387230</td>\n",
       "      <td>-14.039276</td>\n",
       "      <td>-21.744428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-26.066434</td>\n",
       "      <td>-27.391093</td>\n",
       "      <td>-27.065579</td>\n",
       "      <td>-18.487692</td>\n",
       "      <td>-7.898725</td>\n",
       "      <td>2.703818</td>\n",
       "      <td>7.072001</td>\n",
       "      <td>5.160881</td>\n",
       "      <td>-1.741065</td>\n",
       "      <td>-10.623888</td>\n",
       "      <td>-17.517930</td>\n",
       "      <td>-24.805317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85789</th>\n",
       "      <td>-26.463595</td>\n",
       "      <td>-40.683517</td>\n",
       "      <td>-52.579954</td>\n",
       "      <td>-55.379404</td>\n",
       "      <td>-55.807754</td>\n",
       "      <td>-54.853559</td>\n",
       "      <td>-59.087699</td>\n",
       "      <td>-56.469397</td>\n",
       "      <td>-55.209656</td>\n",
       "      <td>-48.277740</td>\n",
       "      <td>-35.060065</td>\n",
       "      <td>-25.579578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85790</th>\n",
       "      <td>-27.124657</td>\n",
       "      <td>-41.571104</td>\n",
       "      <td>-53.739873</td>\n",
       "      <td>-56.269398</td>\n",
       "      <td>-57.174633</td>\n",
       "      <td>-55.735657</td>\n",
       "      <td>-60.054656</td>\n",
       "      <td>-57.688047</td>\n",
       "      <td>-56.440033</td>\n",
       "      <td>-49.348712</td>\n",
       "      <td>-35.752015</td>\n",
       "      <td>-26.136231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85791</th>\n",
       "      <td>-27.232112</td>\n",
       "      <td>-42.150392</td>\n",
       "      <td>-54.230889</td>\n",
       "      <td>-56.695314</td>\n",
       "      <td>-57.873331</td>\n",
       "      <td>-56.092594</td>\n",
       "      <td>-60.519794</td>\n",
       "      <td>-58.166734</td>\n",
       "      <td>-56.951290</td>\n",
       "      <td>-49.948798</td>\n",
       "      <td>-36.002803</td>\n",
       "      <td>-26.365768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85792</th>\n",
       "      <td>-27.196750</td>\n",
       "      <td>-42.375904</td>\n",
       "      <td>-54.335810</td>\n",
       "      <td>-56.658709</td>\n",
       "      <td>-57.935848</td>\n",
       "      <td>-55.935078</td>\n",
       "      <td>-60.454131</td>\n",
       "      <td>-58.170307</td>\n",
       "      <td>-56.905594</td>\n",
       "      <td>-50.349672</td>\n",
       "      <td>-36.119418</td>\n",
       "      <td>-26.257689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85793</th>\n",
       "      <td>-27.096628</td>\n",
       "      <td>-42.534882</td>\n",
       "      <td>-54.032129</td>\n",
       "      <td>-56.255701</td>\n",
       "      <td>-57.715741</td>\n",
       "      <td>-55.434006</td>\n",
       "      <td>-60.061958</td>\n",
       "      <td>-57.965854</td>\n",
       "      <td>-56.579379</td>\n",
       "      <td>-50.706742</td>\n",
       "      <td>-36.491473</td>\n",
       "      <td>-26.294297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85794 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Jan        Feb        Mar        Apr        May        Jun  \\\n",
       "0     -20.961378 -20.779783 -19.675764 -14.790577  -5.732376   0.135406   \n",
       "1     -24.456618 -24.331992 -23.543032 -16.092644  -6.216872   0.726472   \n",
       "2     -25.131482 -25.416651 -24.716702 -16.879098  -7.093584   0.708316   \n",
       "3     -24.033856 -24.646378 -24.136576 -15.999113  -5.939159   3.164843   \n",
       "4     -26.066434 -27.391093 -27.065579 -18.487692  -7.898725   2.703818   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "85789 -26.463595 -40.683517 -52.579954 -55.379404 -55.807754 -54.853559   \n",
       "85790 -27.124657 -41.571104 -53.739873 -56.269398 -57.174633 -55.735657   \n",
       "85791 -27.232112 -42.150392 -54.230889 -56.695314 -57.873331 -56.092594   \n",
       "85792 -27.196750 -42.375904 -54.335810 -56.658709 -57.935848 -55.935078   \n",
       "85793 -27.096628 -42.534882 -54.032129 -56.255701 -57.715741 -55.434006   \n",
       "\n",
       "             Jul        Aug        Sep        Oct        Nov        Dec  \n",
       "0       3.152787   3.225932   0.135928  -4.684637  -9.416071 -18.191157  \n",
       "1       3.691958   3.264084  -0.171904  -6.644039 -12.713751 -21.234518  \n",
       "2       3.854926   3.230236  -0.916411  -7.677704 -14.062823 -22.229460  \n",
       "3       6.835533   5.678750   0.305764  -7.387230 -14.039276 -21.744428  \n",
       "4       7.072001   5.160881  -1.741065 -10.623888 -17.517930 -24.805317  \n",
       "...          ...        ...        ...        ...        ...        ...  \n",
       "85789 -59.087699 -56.469397 -55.209656 -48.277740 -35.060065 -25.579578  \n",
       "85790 -60.054656 -57.688047 -56.440033 -49.348712 -35.752015 -26.136231  \n",
       "85791 -60.519794 -58.166734 -56.951290 -49.948798 -36.002803 -26.365768  \n",
       "85792 -60.454131 -58.170307 -56.905594 -50.349672 -36.119418 -26.257689  \n",
       "85793 -60.061958 -57.965854 -56.579379 -50.706742 -36.491473 -26.294297  \n",
       "\n",
       "[85794 rows x 12 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at our final dataframe where every column has the monthly mean temperature (in C) for that location\n",
    "# Every row is a new location\n",
    "dfTemp = pd.DataFrame(tempp, columns = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \n",
    "                                                    \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \n",
    "                                                    \"Nov\", \"Dec\"])\n",
    "\n",
    "dfTemp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finding Mean Monthly Precipitation for all locations:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the mean precipitation of all months over these 115 years\n",
    "# means[0] = mean of all January precipitation for a location\n",
    "# .\n",
    "# .\n",
    "# .\n",
    "# means[11] = mean of all December precipitation for a location\n",
    "ef = pd.read_csv('precip.1900', sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan\", \"Feb\", \"Mar\", \"Apr\", \n",
    "                                                    \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \n",
    "                                                    \"Nov\", \"Dec\"])\n",
    "avgsP = []\n",
    "avgsP.append(ef[\"Jan\"]) \n",
    "avgsP[0]\n",
    "\n",
    "# This for loop goes from files of year 1901 to 2014\n",
    "for x in range(1901,2015):\n",
    "    yearS = str(x)\n",
    "    tempDf = pd.read_csv('precip.'+yearS, sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan\", \"Feb\", \"Mar\", \"Apr\", \n",
    "                                                    \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \n",
    "                                                    \"Nov\", \"Dec\"])\n",
    "    # Appending every January to the list of means and finding its average \n",
    "    neans = []\n",
    "    neans.append(tempDf[\"Jan\"]) \n",
    "    neans[0]\n",
    "    avgsP[0] = [(g + h) / 2 for g, h in zip(avgsP[0], neans[0])]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's find the mean for all of the remaining months\n",
    "listMonths = [\"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
    "for m in listMonths:\n",
    "    avgsP.append(ef[m]) \n",
    "# means[0]\n",
    "\n",
    "# This for loop goes from files of year 1901 to 2014\n",
    "for x in range(1901,2015):\n",
    "    yearS = str(x)\n",
    "    tempDf = pd.read_csv('precip.'+yearS, sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan\", \"Feb\", \"Mar\", \"Apr\", \n",
    "                                                    \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \n",
    "                                                    \"Nov\", \"Dec\"])\n",
    "    # Appending every month to the list of means and finding its average \n",
    "    neans = []\n",
    "    for m in listMonths:\n",
    "        neans.append(tempDf[m])\n",
    "    for i in range(1,12):\n",
    "        avgsP[i] = [(g + h) / 2 for g, h in zip(avgsP[i], neans[i-1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.906886</td>\n",
       "      <td>11.710341</td>\n",
       "      <td>1.247260e+01</td>\n",
       "      <td>4.905435</td>\n",
       "      <td>3.059161</td>\n",
       "      <td>5.958738e+00</td>\n",
       "      <td>1.024784e+01</td>\n",
       "      <td>1.200584e+01</td>\n",
       "      <td>1.559985e+01</td>\n",
       "      <td>1.147546e+01</td>\n",
       "      <td>10.228974</td>\n",
       "      <td>8.500942e+00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.772241</td>\n",
       "      <td>22.034911</td>\n",
       "      <td>1.751727e+01</td>\n",
       "      <td>9.456831</td>\n",
       "      <td>23.178640</td>\n",
       "      <td>1.534989e+01</td>\n",
       "      <td>2.868153e+01</td>\n",
       "      <td>3.378672e+01</td>\n",
       "      <td>4.942416e+01</td>\n",
       "      <td>4.055705e+01</td>\n",
       "      <td>30.885323</td>\n",
       "      <td>2.619417e+01</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.057248</td>\n",
       "      <td>25.427971</td>\n",
       "      <td>1.857660e+01</td>\n",
       "      <td>9.217719</td>\n",
       "      <td>29.491328</td>\n",
       "      <td>1.529094e+01</td>\n",
       "      <td>3.255547e+01</td>\n",
       "      <td>3.415742e+01</td>\n",
       "      <td>4.155990e+01</td>\n",
       "      <td>4.043054e+01</td>\n",
       "      <td>31.840381</td>\n",
       "      <td>2.742965e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.724294</td>\n",
       "      <td>31.681006</td>\n",
       "      <td>2.110698e+01</td>\n",
       "      <td>10.539274</td>\n",
       "      <td>37.674670</td>\n",
       "      <td>1.769305e+01</td>\n",
       "      <td>4.213071e+01</td>\n",
       "      <td>3.787658e+01</td>\n",
       "      <td>3.814262e+01</td>\n",
       "      <td>3.952282e+01</td>\n",
       "      <td>34.343405</td>\n",
       "      <td>3.151094e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.887307</td>\n",
       "      <td>38.706987</td>\n",
       "      <td>2.434751e+01</td>\n",
       "      <td>12.559512</td>\n",
       "      <td>42.270961</td>\n",
       "      <td>2.022752e+01</td>\n",
       "      <td>5.418735e+01</td>\n",
       "      <td>4.210935e+01</td>\n",
       "      <td>3.707040e+01</td>\n",
       "      <td>3.738716e+01</td>\n",
       "      <td>39.224153</td>\n",
       "      <td>3.759461e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>85789</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.009071</td>\n",
       "      <td>2.372787e-07</td>\n",
       "      <td>0.010156</td>\n",
       "      <td>0.022266</td>\n",
       "      <td>3.439163e-09</td>\n",
       "      <td>2.270498e-07</td>\n",
       "      <td>9.894113e-10</td>\n",
       "      <td>3.434279e-09</td>\n",
       "      <td>2.443962e-05</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>2.502983e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85790</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.006238</td>\n",
       "      <td>2.258466e-07</td>\n",
       "      <td>0.009570</td>\n",
       "      <td>0.021680</td>\n",
       "      <td>2.372254e-09</td>\n",
       "      <td>1.434443e-07</td>\n",
       "      <td>7.513912e-10</td>\n",
       "      <td>2.667238e-09</td>\n",
       "      <td>1.224385e-05</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>3.126629e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85791</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.004395</td>\n",
       "      <td>3.908398e-04</td>\n",
       "      <td>0.009180</td>\n",
       "      <td>0.021289</td>\n",
       "      <td>1.491885e-09</td>\n",
       "      <td>8.368202e-08</td>\n",
       "      <td>5.254227e-10</td>\n",
       "      <td>1.902378e-09</td>\n",
       "      <td>4.835544e-08</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>1.562958e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85792</th>\n",
       "      <td>0.100098</td>\n",
       "      <td>0.002478</td>\n",
       "      <td>3.125206e-03</td>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.021289</td>\n",
       "      <td>8.120668e-10</td>\n",
       "      <td>4.784343e-08</td>\n",
       "      <td>2.965450e-10</td>\n",
       "      <td>1.144090e-09</td>\n",
       "      <td>4.806924e-08</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>1.850785e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85793</th>\n",
       "      <td>0.100902</td>\n",
       "      <td>0.002246</td>\n",
       "      <td>6.250100e-03</td>\n",
       "      <td>0.008008</td>\n",
       "      <td>0.021289</td>\n",
       "      <td>2.587613e-10</td>\n",
       "      <td>1.197063e-08</td>\n",
       "      <td>1.055027e-10</td>\n",
       "      <td>3.799821e-10</td>\n",
       "      <td>1.201745e-08</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>2.999051e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85794 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Jan        Feb           Mar        Apr        May           Jun  \\\n",
       "0      17.906886  11.710341  1.247260e+01   4.905435   3.059161  5.958738e+00   \n",
       "1      27.772241  22.034911  1.751727e+01   9.456831  23.178640  1.534989e+01   \n",
       "2      28.057248  25.427971  1.857660e+01   9.217719  29.491328  1.529094e+01   \n",
       "3      30.724294  31.681006  2.110698e+01  10.539274  37.674670  1.769305e+01   \n",
       "4      33.887307  38.706987  2.434751e+01  12.559512  42.270961  2.022752e+01   \n",
       "...          ...        ...           ...        ...        ...           ...   \n",
       "85789   0.050000   0.009071  2.372787e-07   0.010156   0.022266  3.439163e-09   \n",
       "85790   0.100000   0.006238  2.258466e-07   0.009570   0.021680  2.372254e-09   \n",
       "85791   0.100000   0.004395  3.908398e-04   0.009180   0.021289  1.491885e-09   \n",
       "85792   0.100098   0.002478  3.125206e-03   0.008594   0.021289  8.120668e-10   \n",
       "85793   0.100902   0.002246  6.250100e-03   0.008008   0.021289  2.587613e-10   \n",
       "\n",
       "                Jul           Aug           Sep           Oct        Nov  \\\n",
       "0      1.024784e+01  1.200584e+01  1.559985e+01  1.147546e+01  10.228974   \n",
       "1      2.868153e+01  3.378672e+01  4.942416e+01  4.055705e+01  30.885323   \n",
       "2      3.255547e+01  3.415742e+01  4.155990e+01  4.043054e+01  31.840381   \n",
       "3      4.213071e+01  3.787658e+01  3.814262e+01  3.952282e+01  34.343405   \n",
       "4      5.418735e+01  4.210935e+01  3.707040e+01  3.738716e+01  39.224153   \n",
       "...             ...           ...           ...           ...        ...   \n",
       "85789  2.270498e-07  9.894113e-10  3.434279e-09  2.443962e-05   0.000303   \n",
       "85790  1.434443e-07  7.513912e-10  2.667238e-09  1.224385e-05   0.000297   \n",
       "85791  8.368202e-08  5.254227e-10  1.902378e-09  4.835544e-08   0.000295   \n",
       "85792  4.784343e-08  2.965450e-10  1.144090e-09  4.806924e-08   0.000294   \n",
       "85793  1.197063e-08  1.055027e-10  3.799821e-10  1.201745e-08   0.000196   \n",
       "\n",
       "                Dec  \n",
       "0      8.500942e+00  \n",
       "1      2.619417e+01  \n",
       "2      2.742965e+01  \n",
       "3      3.151094e+01  \n",
       "4      3.759461e+01  \n",
       "...             ...  \n",
       "85789  2.502983e-07  \n",
       "85790  3.126629e-03  \n",
       "85791  1.562958e-02  \n",
       "85792  1.850785e-01  \n",
       "85793  2.999051e-01  \n",
       "\n",
       "[85794 rows x 12 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting all these means of months to list of columns\n",
    "precipp = list(zip(avgsP[0],avgsP[1],avgsP[2],avgsP[3],avgsP[4],avgsP[5],avgsP[6],avgsP[7],avgsP[8],avgsP[9]\n",
    "        ,avgsP[10],avgsP[11] ) )\n",
    "\n",
    "\n",
    "# Let's look at our final dataframe where every column has the monthly mean precipiation (in mm) for that location\n",
    "# Every row is a new location\n",
    "dfPrecip = pd.DataFrame(precipp, columns = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \n",
    "                                                    \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \n",
    "                                                    \"Nov\", \"Dec\"])\n",
    "\n",
    "dfPrecip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Putting all Temperature data together for all locations for our EDA:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 85794 entries, 0 to 85793\n",
      "Columns: 1382 entries, Longitude to Dec 2014\n",
      "dtypes: float64(1382)\n",
      "memory usage: 904.6 MB\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Jan 1900</th>\n",
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       "      <th>Mar 1900</th>\n",
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       "      <th>Sep 2014</th>\n",
       "      <th>Oct 2014</th>\n",
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       "      <th>Dec 2014</th>\n",
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       "      <th>0</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>71.25</td>\n",
       "      <td>-26.9</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-22.6</td>\n",
       "      <td>-22.4</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-15.2</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>-19.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.75</td>\n",
       "      <td>-28.7</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>-24.5</td>\n",
       "      <td>-24.4</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>-22.7</td>\n",
       "      <td>-16.1</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>-8.8</td>\n",
       "      <td>-22.2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.25</td>\n",
       "      <td>-29.3</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>-25.4</td>\n",
       "      <td>-25.2</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>-24.1</td>\n",
       "      <td>-16.9</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.75</td>\n",
       "      <td>-28.1</td>\n",
       "      <td>-20.8</td>\n",
       "      <td>-24.3</td>\n",
       "      <td>-24.3</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-23.6</td>\n",
       "      <td>-15.9</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>-21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.25</td>\n",
       "      <td>-30.1</td>\n",
       "      <td>-23.3</td>\n",
       "      <td>-26.8</td>\n",
       "      <td>-26.7</td>\n",
       "      <td>-9.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-26.4</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>-13.3</td>\n",
       "      <td>-24.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1382 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Longitude  Latitude  Jan 1900  Feb 1900  Mar 1900  Apr 1900  May 1900  \\\n",
       "0    -179.75     71.25     -26.9     -19.0     -22.6     -22.4      -8.0   \n",
       "1    -179.75     68.75     -28.7     -21.2     -24.5     -24.4      -8.0   \n",
       "2    -179.75     68.25     -29.3     -22.0     -25.4     -25.2      -8.7   \n",
       "3    -179.75     67.75     -28.1     -20.8     -24.3     -24.3      -7.4   \n",
       "4    -179.75     67.25     -30.1     -23.3     -26.8     -26.7      -9.1   \n",
       "\n",
       "   Jun 1900  Jul 1900  Aug 1900  ...  Mar 2014  Apr 2014  May 2014  Jun 2014  \\\n",
       "0      -0.8       2.6       0.3  ...     -19.0     -15.2      -5.1      -0.7   \n",
       "1       0.0       3.9       0.9  ...     -22.7     -16.1      -6.0      -0.2   \n",
       "2       0.0       4.1       0.9  ...     -24.1     -16.9      -7.0      -0.2   \n",
       "3       2.5       7.2       3.5  ...     -23.6     -15.9      -5.8       2.3   \n",
       "4       2.2       7.5       3.1  ...     -26.4     -18.2      -7.7       1.8   \n",
       "\n",
       "   Jul 2014  Aug 2014  Sep 2014  Oct 2014  Nov 2014  Dec 2014  \n",
       "0       2.9       4.8       0.9      -3.5      -5.3     -19.0  \n",
       "1       4.0       5.0       0.9      -5.3      -8.8     -22.2  \n",
       "2       4.1       4.9       0.2      -6.6     -10.0     -22.7  \n",
       "3       7.1       7.3       1.5      -6.6      -9.9     -21.7  \n",
       "4       7.3       6.7      -0.5     -10.1     -13.3     -24.3  \n",
       "\n",
       "[5 rows x 1382 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Open each of the 115 files and add headers to them. Then we concatenate them.\n",
    "df = pd.read_csv('air_temp.1900', sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan 1900\", \"Feb 1900\", \"Mar 1900\", \"Apr 1900\", \n",
    "                                                    \"May 1900\", \"Jun 1900\", \"Jul 1900\", \"Aug 1900\", \"Sep 1900\", \"Oct 1900\", \n",
    "                                                    \"Nov 1900\", \"Dec 1900\"])\n",
    "\n",
    "# This for loop goes from files of year 1901 to 2014\n",
    "for x in range(1901,2015):\n",
    "    yearS = str(x)\n",
    "    tempDf = pd.read_csv('air_temp.'+yearS, sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan \"+yearS, \"Feb \"+yearS, \"Mar \"+yearS,\n",
    "                                                              \"Apr \"+yearS, \"May \"+yearS, \"Jun \"+yearS, \"Jul \"+yearS, \n",
    "                                                              \"Aug \"+yearS, \"Sep \"+yearS, \"Oct \"+yearS, \"Nov \"+yearS, \n",
    "                                                              \"Dec \"+yearS])\n",
    "    df = pd.concat([df, tempDf], axis=1)\n",
    "\n",
    "# Removing duplicate columns (of latitude and longitude)    \n",
    "df1 = df.loc[:, ~df.columns.duplicated()]\n",
    "\n",
    "df1.info()\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.loc[:, ~df.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We thus see that we have no null values in our data set. <br>\n",
    "Also, given the way our dataset is defined, all of the 85794 locations are unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 85794 entries, 0 to 85793\n",
      "Columns: 1382 entries, Longitude to Dec 2014\n",
      "dtypes: float64(1382)\n",
      "memory usage: 904.6 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Jan 1900</th>\n",
       "      <th>Feb 1900</th>\n",
       "      <th>Mar 1900</th>\n",
       "      <th>Apr 1900</th>\n",
       "      <th>May 1900</th>\n",
       "      <th>Jun 1900</th>\n",
       "      <th>Jul 1900</th>\n",
       "      <th>Aug 1900</th>\n",
       "      <th>...</th>\n",
       "      <th>Mar 2014</th>\n",
       "      <th>Apr 2014</th>\n",
       "      <th>May 2014</th>\n",
       "      <th>Jun 2014</th>\n",
       "      <th>Jul 2014</th>\n",
       "      <th>Aug 2014</th>\n",
       "      <th>Sep 2014</th>\n",
       "      <th>Oct 2014</th>\n",
       "      <th>Nov 2014</th>\n",
       "      <th>Dec 2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>71.25</td>\n",
       "      <td>-26.9</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-22.6</td>\n",
       "      <td>-22.4</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-15.2</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>-19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.75</td>\n",
       "      <td>-28.7</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>-24.5</td>\n",
       "      <td>-24.4</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>-22.7</td>\n",
       "      <td>-16.1</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>-8.8</td>\n",
       "      <td>-22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.25</td>\n",
       "      <td>-29.3</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>-25.4</td>\n",
       "      <td>-25.2</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>-24.1</td>\n",
       "      <td>-16.9</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.75</td>\n",
       "      <td>-28.1</td>\n",
       "      <td>-20.8</td>\n",
       "      <td>-24.3</td>\n",
       "      <td>-24.3</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-23.6</td>\n",
       "      <td>-15.9</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>-21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.25</td>\n",
       "      <td>-30.1</td>\n",
       "      <td>-23.3</td>\n",
       "      <td>-26.8</td>\n",
       "      <td>-26.7</td>\n",
       "      <td>-9.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-26.4</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>-13.3</td>\n",
       "      <td>-24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>66.75</td>\n",
       "      <td>-26.1</td>\n",
       "      <td>-18.7</td>\n",
       "      <td>-22.6</td>\n",
       "      <td>-22.9</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>...</td>\n",
       "      <td>-22.1</td>\n",
       "      <td>-14.1</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>-19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>66.25</td>\n",
       "      <td>-21.6</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>-17.2</td>\n",
       "      <td>-18.9</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>4.7</td>\n",
       "      <td>9.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-16.7</td>\n",
       "      <td>-9.8</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-4.7</td>\n",
       "      <td>-14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>65.75</td>\n",
       "      <td>-20.6</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-16.7</td>\n",
       "      <td>-17.8</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>11.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>...</td>\n",
       "      <td>-16.8</td>\n",
       "      <td>-8.9</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>11.2</td>\n",
       "      <td>12.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>65.25</td>\n",
       "      <td>-20.8</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>-16.7</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>5.9</td>\n",
       "      <td>11.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>-9.3</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.7</td>\n",
       "      <td>13.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-16.75</td>\n",
       "      <td>27.2</td>\n",
       "      <td>27.2</td>\n",
       "      <td>27.1</td>\n",
       "      <td>26.7</td>\n",
       "      <td>25.1</td>\n",
       "      <td>24.4</td>\n",
       "      <td>24.1</td>\n",
       "      <td>24.8</td>\n",
       "      <td>...</td>\n",
       "      <td>28.8</td>\n",
       "      <td>27.8</td>\n",
       "      <td>27.1</td>\n",
       "      <td>26.6</td>\n",
       "      <td>24.2</td>\n",
       "      <td>24.5</td>\n",
       "      <td>26.6</td>\n",
       "      <td>27.1</td>\n",
       "      <td>27.7</td>\n",
       "      <td>27.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-84.75</td>\n",
       "      <td>-20.2</td>\n",
       "      <td>-30.2</td>\n",
       "      <td>-39.3</td>\n",
       "      <td>-43.4</td>\n",
       "      <td>-44.7</td>\n",
       "      <td>-45.5</td>\n",
       "      <td>-48.7</td>\n",
       "      <td>-48.9</td>\n",
       "      <td>...</td>\n",
       "      <td>-40.7</td>\n",
       "      <td>-42.3</td>\n",
       "      <td>-40.3</td>\n",
       "      <td>-44.3</td>\n",
       "      <td>-49.8</td>\n",
       "      <td>-44.9</td>\n",
       "      <td>-41.5</td>\n",
       "      <td>-36.3</td>\n",
       "      <td>-27.3</td>\n",
       "      <td>-19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-85.25</td>\n",
       "      <td>-25.1</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>-45.7</td>\n",
       "      <td>-49.7</td>\n",
       "      <td>-50.9</td>\n",
       "      <td>-51.4</td>\n",
       "      <td>-54.9</td>\n",
       "      <td>-54.4</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.3</td>\n",
       "      <td>-48.4</td>\n",
       "      <td>-46.6</td>\n",
       "      <td>-50.1</td>\n",
       "      <td>-55.6</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>-48.0</td>\n",
       "      <td>-42.6</td>\n",
       "      <td>-32.5</td>\n",
       "      <td>-24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-85.75</td>\n",
       "      <td>-23.9</td>\n",
       "      <td>-35.5</td>\n",
       "      <td>-45.8</td>\n",
       "      <td>-49.7</td>\n",
       "      <td>-50.7</td>\n",
       "      <td>-50.9</td>\n",
       "      <td>-54.7</td>\n",
       "      <td>-53.8</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.5</td>\n",
       "      <td>-48.3</td>\n",
       "      <td>-46.6</td>\n",
       "      <td>-49.4</td>\n",
       "      <td>-55.1</td>\n",
       "      <td>-50.5</td>\n",
       "      <td>-48.3</td>\n",
       "      <td>-42.5</td>\n",
       "      <td>-31.5</td>\n",
       "      <td>-23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-86.25</td>\n",
       "      <td>-25.4</td>\n",
       "      <td>-37.3</td>\n",
       "      <td>-48.3</td>\n",
       "      <td>-52.1</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>-57.1</td>\n",
       "      <td>-55.6</td>\n",
       "      <td>...</td>\n",
       "      <td>-50.2</td>\n",
       "      <td>-50.5</td>\n",
       "      <td>-49.1</td>\n",
       "      <td>-51.4</td>\n",
       "      <td>-57.2</td>\n",
       "      <td>-52.7</td>\n",
       "      <td>-50.9</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>-24.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-86.75</td>\n",
       "      <td>-26.5</td>\n",
       "      <td>-38.8</td>\n",
       "      <td>-50.1</td>\n",
       "      <td>-53.8</td>\n",
       "      <td>-54.7</td>\n",
       "      <td>-54.7</td>\n",
       "      <td>-58.9</td>\n",
       "      <td>-57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-52.1</td>\n",
       "      <td>-52.1</td>\n",
       "      <td>-51.0</td>\n",
       "      <td>-52.9</td>\n",
       "      <td>-58.8</td>\n",
       "      <td>-54.6</td>\n",
       "      <td>-52.9</td>\n",
       "      <td>-46.7</td>\n",
       "      <td>-34.2</td>\n",
       "      <td>-25.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-87.25</td>\n",
       "      <td>-27.2</td>\n",
       "      <td>-39.6</td>\n",
       "      <td>-51.3</td>\n",
       "      <td>-54.9</td>\n",
       "      <td>-55.7</td>\n",
       "      <td>-55.6</td>\n",
       "      <td>-60.0</td>\n",
       "      <td>-58.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-53.5</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>-52.2</td>\n",
       "      <td>-53.7</td>\n",
       "      <td>-59.6</td>\n",
       "      <td>-56.1</td>\n",
       "      <td>-54.5</td>\n",
       "      <td>-47.9</td>\n",
       "      <td>-34.9</td>\n",
       "      <td>-25.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-87.75</td>\n",
       "      <td>-27.7</td>\n",
       "      <td>-40.4</td>\n",
       "      <td>-52.2</td>\n",
       "      <td>-55.7</td>\n",
       "      <td>-56.7</td>\n",
       "      <td>-56.6</td>\n",
       "      <td>-60.9</td>\n",
       "      <td>-58.9</td>\n",
       "      <td>...</td>\n",
       "      <td>-54.5</td>\n",
       "      <td>-53.6</td>\n",
       "      <td>-53.3</td>\n",
       "      <td>-54.6</td>\n",
       "      <td>-60.4</td>\n",
       "      <td>-57.3</td>\n",
       "      <td>-55.7</td>\n",
       "      <td>-48.8</td>\n",
       "      <td>-35.4</td>\n",
       "      <td>-26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-88.25</td>\n",
       "      <td>-28.2</td>\n",
       "      <td>-41.1</td>\n",
       "      <td>-53.4</td>\n",
       "      <td>-56.8</td>\n",
       "      <td>-57.7</td>\n",
       "      <td>-57.7</td>\n",
       "      <td>-62.1</td>\n",
       "      <td>-59.8</td>\n",
       "      <td>...</td>\n",
       "      <td>-55.8</td>\n",
       "      <td>-54.5</td>\n",
       "      <td>-54.6</td>\n",
       "      <td>-55.4</td>\n",
       "      <td>-61.4</td>\n",
       "      <td>-58.5</td>\n",
       "      <td>-57.2</td>\n",
       "      <td>-49.9</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>-26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-88.75</td>\n",
       "      <td>-28.3</td>\n",
       "      <td>-41.5</td>\n",
       "      <td>-53.9</td>\n",
       "      <td>-57.3</td>\n",
       "      <td>-58.1</td>\n",
       "      <td>-58.2</td>\n",
       "      <td>-62.6</td>\n",
       "      <td>-60.3</td>\n",
       "      <td>...</td>\n",
       "      <td>-56.4</td>\n",
       "      <td>-54.8</td>\n",
       "      <td>-55.1</td>\n",
       "      <td>-55.7</td>\n",
       "      <td>-61.8</td>\n",
       "      <td>-59.3</td>\n",
       "      <td>-57.9</td>\n",
       "      <td>-50.5</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>-26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-89.25</td>\n",
       "      <td>-28.1</td>\n",
       "      <td>-41.4</td>\n",
       "      <td>-54.1</td>\n",
       "      <td>-57.3</td>\n",
       "      <td>-58.1</td>\n",
       "      <td>-58.4</td>\n",
       "      <td>-62.6</td>\n",
       "      <td>-60.3</td>\n",
       "      <td>...</td>\n",
       "      <td>-56.6</td>\n",
       "      <td>-54.6</td>\n",
       "      <td>-55.2</td>\n",
       "      <td>-55.5</td>\n",
       "      <td>-61.7</td>\n",
       "      <td>-59.4</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>-36.7</td>\n",
       "      <td>-26.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 1382 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Longitude  Latitude  Jan 1900  Feb 1900  Mar 1900  Apr 1900  May 1900  \\\n",
       "0     -179.75     71.25     -26.9     -19.0     -22.6     -22.4      -8.0   \n",
       "1     -179.75     68.75     -28.7     -21.2     -24.5     -24.4      -8.0   \n",
       "2     -179.75     68.25     -29.3     -22.0     -25.4     -25.2      -8.7   \n",
       "3     -179.75     67.75     -28.1     -20.8     -24.3     -24.3      -7.4   \n",
       "4     -179.75     67.25     -30.1     -23.3     -26.8     -26.7      -9.1   \n",
       "5     -179.75     66.75     -26.1     -18.7     -22.6     -22.9      -6.1   \n",
       "6     -179.75     66.25     -21.6     -13.4     -17.2     -18.9      -3.5   \n",
       "7     -179.75     65.75     -20.6     -12.0     -16.7     -17.8      -2.9   \n",
       "8     -179.75     65.25     -20.8     -12.2     -16.7     -18.2      -2.7   \n",
       "9     -179.75    -16.75      27.2      27.2      27.1      26.7      25.1   \n",
       "10    -179.75    -84.75     -20.2     -30.2     -39.3     -43.4     -44.7   \n",
       "11    -179.75    -85.25     -25.1     -36.0     -45.7     -49.7     -50.9   \n",
       "12    -179.75    -85.75     -23.9     -35.5     -45.8     -49.7     -50.7   \n",
       "13    -179.75    -86.25     -25.4     -37.3     -48.3     -52.1     -53.0   \n",
       "14    -179.75    -86.75     -26.5     -38.8     -50.1     -53.8     -54.7   \n",
       "15    -179.75    -87.25     -27.2     -39.6     -51.3     -54.9     -55.7   \n",
       "16    -179.75    -87.75     -27.7     -40.4     -52.2     -55.7     -56.7   \n",
       "17    -179.75    -88.25     -28.2     -41.1     -53.4     -56.8     -57.7   \n",
       "18    -179.75    -88.75     -28.3     -41.5     -53.9     -57.3     -58.1   \n",
       "19    -179.75    -89.25     -28.1     -41.4     -54.1     -57.3     -58.1   \n",
       "\n",
       "    Jun 1900  Jul 1900  Aug 1900  ...  Mar 2014  Apr 2014  May 2014  Jun 2014  \\\n",
       "0       -0.8       2.6       0.3  ...     -19.0     -15.2      -5.1      -0.7   \n",
       "1        0.0       3.9       0.9  ...     -22.7     -16.1      -6.0      -0.2   \n",
       "2        0.0       4.1       0.9  ...     -24.1     -16.9      -7.0      -0.2   \n",
       "3        2.5       7.2       3.5  ...     -23.6     -15.9      -5.8       2.3   \n",
       "4        2.2       7.5       3.1  ...     -26.4     -18.2      -7.7       1.8   \n",
       "5        4.3       9.6       5.8  ...     -22.1     -14.1      -5.0       3.5   \n",
       "6        4.7       9.9       7.1  ...     -16.7      -9.8      -2.6       3.4   \n",
       "7        5.7      11.1       8.7  ...     -16.8      -8.9      -1.4       5.3   \n",
       "8        5.9      11.6       9.1  ...     -17.4      -9.3      -0.6       6.4   \n",
       "9       24.4      24.1      24.8  ...      28.8      27.8      27.1      26.6   \n",
       "10     -45.5     -48.7     -48.9  ...     -40.7     -42.3     -40.3     -44.3   \n",
       "11     -51.4     -54.9     -54.4  ...     -47.3     -48.4     -46.6     -50.1   \n",
       "12     -50.9     -54.7     -53.8  ...     -47.5     -48.3     -46.6     -49.4   \n",
       "13     -53.0     -57.1     -55.6  ...     -50.2     -50.5     -49.1     -51.4   \n",
       "14     -54.7     -58.9     -57.0  ...     -52.1     -52.1     -51.0     -52.9   \n",
       "15     -55.6     -60.0     -58.1  ...     -53.5     -53.0     -52.2     -53.7   \n",
       "16     -56.6     -60.9     -58.9  ...     -54.5     -53.6     -53.3     -54.6   \n",
       "17     -57.7     -62.1     -59.8  ...     -55.8     -54.5     -54.6     -55.4   \n",
       "18     -58.2     -62.6     -60.3  ...     -56.4     -54.8     -55.1     -55.7   \n",
       "19     -58.4     -62.6     -60.3  ...     -56.6     -54.6     -55.2     -55.5   \n",
       "\n",
       "    Jul 2014  Aug 2014  Sep 2014  Oct 2014  Nov 2014  Dec 2014  \n",
       "0        2.9       4.8       0.9      -3.5      -5.3     -19.0  \n",
       "1        4.0       5.0       0.9      -5.3      -8.8     -22.2  \n",
       "2        4.1       4.9       0.2      -6.6     -10.0     -22.7  \n",
       "3        7.1       7.3       1.5      -6.6      -9.9     -21.7  \n",
       "4        7.3       6.7      -0.5     -10.1     -13.3     -24.3  \n",
       "5        8.9       9.0       2.1      -6.7      -9.2     -19.9  \n",
       "6        9.0      10.2       4.1      -3.0      -4.7     -14.6  \n",
       "7       11.2      12.3       5.7      -1.5      -4.0     -13.8  \n",
       "8       12.7      13.2       6.3      -1.6      -4.6     -14.5  \n",
       "9       24.2      24.5      26.6      27.1      27.7      27.7  \n",
       "10     -49.8     -44.9     -41.5     -36.3     -27.3     -19.5  \n",
       "11     -55.6     -50.8     -48.0     -42.6     -32.5     -24.3  \n",
       "12     -55.1     -50.5     -48.3     -42.5     -31.5     -23.0  \n",
       "13     -57.2     -52.7     -50.9     -45.0     -33.0     -24.2  \n",
       "14     -58.8     -54.6     -52.9     -46.7     -34.2     -25.2  \n",
       "15     -59.6     -56.1     -54.5     -47.9     -34.9     -25.7  \n",
       "16     -60.4     -57.3     -55.7     -48.8     -35.4     -26.1  \n",
       "17     -61.4     -58.5     -57.2     -49.9     -36.1     -26.6  \n",
       "18     -61.8     -59.3     -57.9     -50.5     -36.4     -26.8  \n",
       "19     -61.7     -59.4     -58.0     -50.8     -36.7     -26.7  \n",
       "\n",
       "[20 rows x 1382 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.info()\n",
    "df1.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_csv(\"cleaned_MonthlyAirTemperature_Jan1900_to_Dec2014.csv\", index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Jan 1900</th>\n",
       "      <th>Feb 1900</th>\n",
       "      <th>Mar 1900</th>\n",
       "      <th>Apr 1900</th>\n",
       "      <th>May 1900</th>\n",
       "      <th>Jun 1900</th>\n",
       "      <th>Jul 1900</th>\n",
       "      <th>Aug 1900</th>\n",
       "      <th>...</th>\n",
       "      <th>Mar 2014</th>\n",
       "      <th>Apr 2014</th>\n",
       "      <th>May 2014</th>\n",
       "      <th>Jun 2014</th>\n",
       "      <th>Jul 2014</th>\n",
       "      <th>Aug 2014</th>\n",
       "      <th>Sep 2014</th>\n",
       "      <th>Oct 2014</th>\n",
       "      <th>Nov 2014</th>\n",
       "      <th>Dec 2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.436540</td>\n",
       "      <td>1.060109</td>\n",
       "      <td>-8.415361</td>\n",
       "      <td>-9.044871</td>\n",
       "      <td>-8.795384</td>\n",
       "      <td>-6.884105</td>\n",
       "      <td>-3.749948</td>\n",
       "      <td>-1.542897</td>\n",
       "      <td>-1.246522</td>\n",
       "      <td>-1.607790</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.996130</td>\n",
       "      <td>-5.943503</td>\n",
       "      <td>-2.453363</td>\n",
       "      <td>-0.877489</td>\n",
       "      <td>-0.803736</td>\n",
       "      <td>-0.680142</td>\n",
       "      <td>-2.226571</td>\n",
       "      <td>-4.361294</td>\n",
       "      <td>-5.511664</td>\n",
       "      <td>-5.637958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>88.008337</td>\n",
       "      <td>57.848430</td>\n",
       "      <td>22.194745</td>\n",
       "      <td>23.872740</td>\n",
       "      <td>26.675315</td>\n",
       "      <td>28.543110</td>\n",
       "      <td>29.495691</td>\n",
       "      <td>30.559122</td>\n",
       "      <td>32.252617</td>\n",
       "      <td>31.792055</td>\n",
       "      <td>...</td>\n",
       "      <td>27.768157</td>\n",
       "      <td>28.677462</td>\n",
       "      <td>28.835514</td>\n",
       "      <td>30.668655</td>\n",
       "      <td>32.535910</td>\n",
       "      <td>31.658684</td>\n",
       "      <td>29.681235</td>\n",
       "      <td>27.034646</td>\n",
       "      <td>22.621675</td>\n",
       "      <td>20.817717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-179.750000</td>\n",
       "      <td>-89.750000</td>\n",
       "      <td>-60.300000</td>\n",
       "      <td>-53.000000</td>\n",
       "      <td>-58.000000</td>\n",
       "      <td>-65.600000</td>\n",
       "      <td>-66.600000</td>\n",
       "      <td>-69.300000</td>\n",
       "      <td>-70.000000</td>\n",
       "      <td>-71.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>-62.300000</td>\n",
       "      <td>-72.900000</td>\n",
       "      <td>-68.600000</td>\n",
       "      <td>-71.300000</td>\n",
       "      <td>-71.900000</td>\n",
       "      <td>-72.900000</td>\n",
       "      <td>-69.600000</td>\n",
       "      <td>-62.700000</td>\n",
       "      <td>-47.100000</td>\n",
       "      <td>-52.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-62.250000</td>\n",
       "      <td>-71.750000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>-28.800000</td>\n",
       "      <td>-27.900000</td>\n",
       "      <td>-28.500000</td>\n",
       "      <td>-28.000000</td>\n",
       "      <td>-29.400000</td>\n",
       "      <td>-32.700000</td>\n",
       "      <td>-32.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>-28.900000</td>\n",
       "      <td>-28.100000</td>\n",
       "      <td>-24.400000</td>\n",
       "      <td>-29.200000</td>\n",
       "      <td>-32.200000</td>\n",
       "      <td>-29.800000</td>\n",
       "      <td>-25.600000</td>\n",
       "      <td>-25.400000</td>\n",
       "      <td>-24.500000</td>\n",
       "      <td>-23.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.250000</td>\n",
       "      <td>19.250000</td>\n",
       "      <td>-14.100000</td>\n",
       "      <td>-15.300000</td>\n",
       "      <td>-8.600000</td>\n",
       "      <td>-1.100000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>-7.900000</td>\n",
       "      <td>-10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>93.750000</td>\n",
       "      <td>52.250000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>16.400000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>14.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179.750000</td>\n",
       "      <td>83.250000</td>\n",
       "      <td>37.300000</td>\n",
       "      <td>37.700000</td>\n",
       "      <td>38.600000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>36.700000</td>\n",
       "      <td>38.100000</td>\n",
       "      <td>39.400000</td>\n",
       "      <td>38.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>37.300000</td>\n",
       "      <td>37.700000</td>\n",
       "      <td>37.900000</td>\n",
       "      <td>39.800000</td>\n",
       "      <td>41.100000</td>\n",
       "      <td>41.400000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>38.300000</td>\n",
       "      <td>38.600000</td>\n",
       "      <td>38.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 1382 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Longitude      Latitude      Jan 1900      Feb 1900      Mar 1900  \\\n",
       "count  85794.000000  85794.000000  85794.000000  85794.000000  85794.000000   \n",
       "mean      18.436540      1.060109     -8.415361     -9.044871     -8.795384   \n",
       "std       88.008337     57.848430     22.194745     23.872740     26.675315   \n",
       "min     -179.750000    -89.750000    -60.300000    -53.000000    -58.000000   \n",
       "25%      -62.250000    -71.750000    -26.900000    -28.800000    -27.900000   \n",
       "50%       28.250000     19.250000    -14.100000    -15.300000     -8.600000   \n",
       "75%       93.750000     52.250000     13.300000     16.400000     18.000000   \n",
       "max      179.750000     83.250000     37.300000     37.700000     38.600000   \n",
       "\n",
       "           Apr 1900      May 1900      Jun 1900      Jul 1900      Aug 1900  \\\n",
       "count  85794.000000  85794.000000  85794.000000  85794.000000  85794.000000   \n",
       "mean      -6.884105     -3.749948     -1.542897     -1.246522     -1.607790   \n",
       "std       28.543110     29.495691     30.559122     32.252617     31.792055   \n",
       "min      -65.600000    -66.600000    -69.300000    -70.000000    -71.400000   \n",
       "25%      -28.500000    -28.000000    -29.400000    -32.700000    -32.800000   \n",
       "50%       -1.100000      6.400000     11.300000     13.500000     12.200000   \n",
       "75%       18.700000     19.600000     21.300000     22.600000     22.500000   \n",
       "max       37.500000     36.700000     38.100000     39.400000     38.100000   \n",
       "\n",
       "       ...      Mar 2014      Apr 2014      May 2014      Jun 2014  \\\n",
       "count  ...  85794.000000  85794.000000  85794.000000  85794.000000   \n",
       "mean   ...     -8.996130     -5.943503     -2.453363     -0.877489   \n",
       "std    ...     27.768157     28.677462     28.835514     30.668655   \n",
       "min    ...    -62.300000    -72.900000    -68.600000    -71.300000   \n",
       "25%    ...    -28.900000    -28.100000    -24.400000    -29.200000   \n",
       "50%    ...     -7.000000      0.900000      7.100000     12.300000   \n",
       "75%    ...     18.100000     19.300000     20.500000     21.900000   \n",
       "max    ...     37.300000     37.700000     37.900000     39.800000   \n",
       "\n",
       "           Jul 2014      Aug 2014      Sep 2014      Oct 2014      Nov 2014  \\\n",
       "count  85794.000000  85794.000000  85794.000000  85794.000000  85794.000000   \n",
       "mean      -0.803736     -0.680142     -2.226571     -4.361294     -5.511664   \n",
       "std       32.535910     31.658684     29.681235     27.034646     22.621675   \n",
       "min      -71.900000    -72.900000    -69.600000    -62.700000    -47.100000   \n",
       "25%      -32.200000    -29.800000    -25.600000    -25.400000    -24.500000   \n",
       "50%       14.300000     13.500000      7.400000      0.300000     -7.900000   \n",
       "75%       22.500000     22.700000     21.800000     20.900000     18.100000   \n",
       "max       41.100000     41.400000     38.500000     38.300000     38.600000   \n",
       "\n",
       "           Dec 2014  \n",
       "count  85794.000000  \n",
       "mean      -5.637958  \n",
       "std       20.817717  \n",
       "min      -52.700000  \n",
       "25%      -23.300000  \n",
       "50%      -10.500000  \n",
       "75%       14.800000  \n",
       "max       38.200000  \n",
       "\n",
       "[8 rows x 1382 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Putting all Precipitation data together for all locations for our EDA:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 85794 entries, 0 to 85793\n",
      "Columns: 1610 entries, Longitude to Dec 2014\n",
      "dtypes: float64(1610)\n",
      "memory usage: 1.0 GB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Jan 1900</th>\n",
       "      <th>Feb 1900</th>\n",
       "      <th>Mar 1900</th>\n",
       "      <th>Apr 1900</th>\n",
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       "      <th>Jun 1900</th>\n",
       "      <th>Jul 1900</th>\n",
       "      <th>Aug 1900</th>\n",
       "      <th>...</th>\n",
       "      <th>Mar 2014</th>\n",
       "      <th>Apr 2014</th>\n",
       "      <th>May 2014</th>\n",
       "      <th>Jun 2014</th>\n",
       "      <th>Jul 2014</th>\n",
       "      <th>Aug 2014</th>\n",
       "      <th>Sep 2014</th>\n",
       "      <th>Oct 2014</th>\n",
       "      <th>Nov 2014</th>\n",
       "      <th>Dec 2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>71.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>12.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37.1</td>\n",
       "      <td>...</td>\n",
       "      <td>10.9</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>10.2</td>\n",
       "      <td>11.1</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.75</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.2</td>\n",
       "      <td>10.9</td>\n",
       "      <td>9.7</td>\n",
       "      <td>19.6</td>\n",
       "      <td>17.7</td>\n",
       "      <td>53.8</td>\n",
       "      <td>...</td>\n",
       "      <td>18.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>31.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>27.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>53.7</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.25</td>\n",
       "      <td>11.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>11.4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>21.2</td>\n",
       "      <td>18.9</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>44.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>31.5</td>\n",
       "      <td>25.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>39.6</td>\n",
       "      <td>42.2</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.75</td>\n",
       "      <td>15.1</td>\n",
       "      <td>11.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>9.3</td>\n",
       "      <td>25.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>61.3</td>\n",
       "      <td>...</td>\n",
       "      <td>24.3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>61.1</td>\n",
       "      <td>11.5</td>\n",
       "      <td>39.9</td>\n",
       "      <td>30.4</td>\n",
       "      <td>43.3</td>\n",
       "      <td>36.9</td>\n",
       "      <td>44.6</td>\n",
       "      <td>33.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.25</td>\n",
       "      <td>20.6</td>\n",
       "      <td>16.7</td>\n",
       "      <td>10.5</td>\n",
       "      <td>16.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>29.9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.5</td>\n",
       "      <td>9.1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>49.8</td>\n",
       "      <td>36.6</td>\n",
       "      <td>44.7</td>\n",
       "      <td>30.6</td>\n",
       "      <td>49.2</td>\n",
       "      <td>35.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Longitude  Latitude  Jan 1900  Feb 1900  Mar 1900  Apr 1900  May 1900  \\\n",
       "0    -179.75     71.25       0.0       1.4       1.9       5.5       6.3   \n",
       "1    -179.75     68.75      12.2       5.6       6.2      10.9       9.7   \n",
       "2    -179.75     68.25      11.7       7.1       6.2      11.4       9.1   \n",
       "3    -179.75     67.75      15.1      11.4       8.0      13.6       9.3   \n",
       "4    -179.75     67.25      20.6      16.7      10.5      16.3       9.5   \n",
       "\n",
       "   Jun 1900  Jul 1900  Aug 1900  ...  Mar 2014  Apr 2014  May 2014  Jun 2014  \\\n",
       "0      12.7       7.7      37.1  ...      10.9       6.9       0.0       5.0   \n",
       "1      19.6      17.7      53.8  ...      18.6       7.4      31.5       8.2   \n",
       "2      21.2      18.9      56.0  ...      20.7       6.5      44.1       8.6   \n",
       "3      25.2      24.2      61.3  ...      24.3       7.4      61.1      11.5   \n",
       "4      29.9      30.0      67.0  ...      28.5       9.1      72.0      13.3   \n",
       "\n",
       "   Jul 2014  Aug 2014  Sep 2014  Oct 2014  Nov 2014  Dec 2014  \n",
       "0       7.3       6.8      22.2      10.2      11.1       8.7  \n",
       "1      27.3      23.3      53.7      40.0      40.7      31.5  \n",
       "2      31.5      25.3      45.5      39.6      42.2      32.1  \n",
       "3      39.9      30.4      43.3      36.9      44.6      33.9  \n",
       "4      49.8      36.6      44.7      30.6      49.2      35.8  \n",
       "\n",
       "[5 rows x 1610 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ef = pd.read_csv('precip.1900', sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan 1900\", \"Feb 1900\", \"Mar 1900\", \"Apr 1900\", \"May 1900\", \"Jun 1900\", \"Jul 1900\", \"Aug 1900\", \"Sep 1900\", \"Oct 1900\", \"Nov 1900\", \"Dec 1900\"])\n",
    "\n",
    "for x in range(1901,2015):\n",
    "    yearS = str(x)\n",
    "    tempEf = pd.read_csv('precip.'+yearS, sep='\\s+', names=[\"Longitude\", \"Latitude\", \"Jan \"+yearS, \"Feb \"+yearS, \"Mar \"+yearS, \"Apr \"+yearS, \"May \"+yearS, \"Jun \"+yearS, \"Jul \"+yearS, \"Aug \"+yearS, \"Sep \"+yearS, \"Oct \"+yearS, \"Nov \"+yearS, \"Dec \"+yearS])\n",
    "    ef = pd.concat([ef, tempEf], axis=1)\n",
    "    \n",
    "ef.info()\n",
    "ef.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ef1 = ef.loc[:, ~ef.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ef1.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We thus see that we have no null values in our data set. <br>\n",
    "Also, given the way our dataset is defined, all of the 85794 locations are unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 85794 entries, 0 to 85793\n",
      "Columns: 1382 entries, Longitude to Dec 2014\n",
      "dtypes: float64(1382)\n",
      "memory usage: 904.6 MB\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Jan 1900</th>\n",
       "      <th>Feb 1900</th>\n",
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       "      <th>Oct 2014</th>\n",
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       "      <th>Dec 2014</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-179.75</td>\n",
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       "      <td>7.7</td>\n",
       "      <td>37.1</td>\n",
       "      <td>...</td>\n",
       "      <td>10.9</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>10.2</td>\n",
       "      <td>11.1</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.75</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.2</td>\n",
       "      <td>10.9</td>\n",
       "      <td>9.7</td>\n",
       "      <td>19.6</td>\n",
       "      <td>17.7</td>\n",
       "      <td>53.8</td>\n",
       "      <td>...</td>\n",
       "      <td>18.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>31.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>27.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>53.7</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>68.25</td>\n",
       "      <td>11.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>11.4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>21.2</td>\n",
       "      <td>18.9</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>44.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>31.5</td>\n",
       "      <td>25.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>39.6</td>\n",
       "      <td>42.2</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.75</td>\n",
       "      <td>15.1</td>\n",
       "      <td>11.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>9.3</td>\n",
       "      <td>25.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>61.3</td>\n",
       "      <td>...</td>\n",
       "      <td>24.3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>61.1</td>\n",
       "      <td>11.5</td>\n",
       "      <td>39.9</td>\n",
       "      <td>30.4</td>\n",
       "      <td>43.3</td>\n",
       "      <td>36.9</td>\n",
       "      <td>44.6</td>\n",
       "      <td>33.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>67.25</td>\n",
       "      <td>20.6</td>\n",
       "      <td>16.7</td>\n",
       "      <td>10.5</td>\n",
       "      <td>16.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>29.9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.5</td>\n",
       "      <td>9.1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>49.8</td>\n",
       "      <td>36.6</td>\n",
       "      <td>44.7</td>\n",
       "      <td>30.6</td>\n",
       "      <td>49.2</td>\n",
       "      <td>35.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>66.75</td>\n",
       "      <td>18.9</td>\n",
       "      <td>16.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>15.3</td>\n",
       "      <td>11.4</td>\n",
       "      <td>34.3</td>\n",
       "      <td>38.9</td>\n",
       "      <td>77.8</td>\n",
       "      <td>...</td>\n",
       "      <td>29.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>86.8</td>\n",
       "      <td>12.5</td>\n",
       "      <td>63.6</td>\n",
       "      <td>49.1</td>\n",
       "      <td>52.3</td>\n",
       "      <td>22.7</td>\n",
       "      <td>50.3</td>\n",
       "      <td>28.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>66.25</td>\n",
       "      <td>14.6</td>\n",
       "      <td>14.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>14.2</td>\n",
       "      <td>15.5</td>\n",
       "      <td>40.9</td>\n",
       "      <td>54.2</td>\n",
       "      <td>94.6</td>\n",
       "      <td>...</td>\n",
       "      <td>29.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>97.3</td>\n",
       "      <td>15.2</td>\n",
       "      <td>81.5</td>\n",
       "      <td>66.4</td>\n",
       "      <td>62.1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>49.7</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>65.75</td>\n",
       "      <td>10.5</td>\n",
       "      <td>12.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>36.4</td>\n",
       "      <td>41.8</td>\n",
       "      <td>87.7</td>\n",
       "      <td>...</td>\n",
       "      <td>24.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>71.1</td>\n",
       "      <td>18.5</td>\n",
       "      <td>61.8</td>\n",
       "      <td>51.6</td>\n",
       "      <td>51.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>65.25</td>\n",
       "      <td>10.4</td>\n",
       "      <td>10.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.9</td>\n",
       "      <td>34.7</td>\n",
       "      <td>34.8</td>\n",
       "      <td>82.1</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>46.9</td>\n",
       "      <td>24.1</td>\n",
       "      <td>47.6</td>\n",
       "      <td>39.9</td>\n",
       "      <td>44.5</td>\n",
       "      <td>24.7</td>\n",
       "      <td>30.1</td>\n",
       "      <td>24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-16.75</td>\n",
       "      <td>384.3</td>\n",
       "      <td>341.8</td>\n",
       "      <td>401.9</td>\n",
       "      <td>214.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>189.9</td>\n",
       "      <td>65.7</td>\n",
       "      <td>244.9</td>\n",
       "      <td>...</td>\n",
       "      <td>278.9</td>\n",
       "      <td>202.1</td>\n",
       "      <td>250.7</td>\n",
       "      <td>157.1</td>\n",
       "      <td>103.2</td>\n",
       "      <td>103.6</td>\n",
       "      <td>61.0</td>\n",
       "      <td>276.2</td>\n",
       "      <td>237.6</td>\n",
       "      <td>350.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-84.75</td>\n",
       "      <td>49.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>...</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-85.25</td>\n",
       "      <td>48.9</td>\n",
       "      <td>47.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>...</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-85.75</td>\n",
       "      <td>47.9</td>\n",
       "      <td>45.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-86.25</td>\n",
       "      <td>47.6</td>\n",
       "      <td>44.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-86.75</td>\n",
       "      <td>46.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-87.25</td>\n",
       "      <td>45.5</td>\n",
       "      <td>41.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-87.75</td>\n",
       "      <td>45.2</td>\n",
       "      <td>41.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-88.25</td>\n",
       "      <td>44.9</td>\n",
       "      <td>41.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-88.75</td>\n",
       "      <td>44.6</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-179.75</td>\n",
       "      <td>-89.25</td>\n",
       "      <td>44.5</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 1382 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Longitude  Latitude  Jan 1900  Feb 1900  Mar 1900  Apr 1900  May 1900  \\\n",
       "0     -179.75     71.25       0.0       1.4       1.9       5.5       6.3   \n",
       "1     -179.75     68.75      12.2       5.6       6.2      10.9       9.7   \n",
       "2     -179.75     68.25      11.7       7.1       6.2      11.4       9.1   \n",
       "3     -179.75     67.75      15.1      11.4       8.0      13.6       9.3   \n",
       "4     -179.75     67.25      20.6      16.7      10.5      16.3       9.5   \n",
       "5     -179.75     66.75      18.9      16.6       9.9      15.3      11.4   \n",
       "6     -179.75     66.25      14.6      14.7       8.9      14.2      15.5   \n",
       "7     -179.75     65.75      10.5      12.5       5.7       9.1       9.1   \n",
       "8     -179.75     65.25      10.4      10.1       5.6       6.9       6.9   \n",
       "9     -179.75    -16.75     384.3     341.8     401.9     214.0      43.2   \n",
       "10    -179.75    -84.75      49.4      49.4       0.0      13.8       1.6   \n",
       "11    -179.75    -85.25      48.9      47.5       0.0      10.6       0.0   \n",
       "12    -179.75    -85.75      47.9      45.3       0.0       7.4       0.0   \n",
       "13    -179.75    -86.25      47.6      44.8       0.0       6.3       0.0   \n",
       "14    -179.75    -86.75      46.0      42.0       0.0       3.5       0.0   \n",
       "15    -179.75    -87.25      45.5      41.6       0.0       2.7       0.0   \n",
       "16    -179.75    -87.75      45.2      41.3       0.0       2.3       0.0   \n",
       "17    -179.75    -88.25      44.9      41.3       0.0       1.8       0.0   \n",
       "18    -179.75    -88.75      44.6      41.2       0.0       1.5       0.0   \n",
       "19    -179.75    -89.25      44.5      41.2       0.0       0.3       0.0   \n",
       "\n",
       "    Jun 1900  Jul 1900  Aug 1900  ...  Mar 2014  Apr 2014  May 2014  Jun 2014  \\\n",
       "0       12.7       7.7      37.1  ...      10.9       6.9       0.0       5.0   \n",
       "1       19.6      17.7      53.8  ...      18.6       7.4      31.5       8.2   \n",
       "2       21.2      18.9      56.0  ...      20.7       6.5      44.1       8.6   \n",
       "3       25.2      24.2      61.3  ...      24.3       7.4      61.1      11.5   \n",
       "4       29.9      30.0      67.0  ...      28.5       9.1      72.0      13.3   \n",
       "5       34.3      38.9      77.8  ...      29.4       7.2      86.8      12.5   \n",
       "6       40.9      54.2      94.6  ...      29.8       5.9      97.3      15.2   \n",
       "7       36.4      41.8      87.7  ...      24.2       6.0      71.1      18.5   \n",
       "8       34.7      34.8      82.1  ...      22.0       8.7      46.9      24.1   \n",
       "9      189.9      65.7     244.9  ...     278.9     202.1     250.7     157.1   \n",
       "10      30.5       0.0      16.2  ...       8.9       2.3      11.5       0.0   \n",
       "11      32.6       0.0      17.4  ...       5.3       0.8       7.7       0.0   \n",
       "12      33.2       0.0      17.8  ...       2.5       0.0       4.5       0.0   \n",
       "13      33.4       0.0      18.2  ...       1.9       0.0       3.7       0.0   \n",
       "14      32.7       0.0      17.9  ...       0.0       0.0       0.6       0.0   \n",
       "15      32.3       0.0      18.1  ...       0.0       0.0       0.1       0.0   \n",
       "16      31.8       0.0      18.1  ...       0.0       0.0       0.0       0.0   \n",
       "17      31.4       0.0      18.1  ...       0.0       0.0       0.0       0.0   \n",
       "18      30.9       0.0      18.2  ...       0.0       0.0       0.0       0.0   \n",
       "19      30.8       0.0      18.2  ...       0.0       0.0       0.0       0.0   \n",
       "\n",
       "    Jul 2014  Aug 2014  Sep 2014  Oct 2014  Nov 2014  Dec 2014  \n",
       "0        7.3       6.8      22.2      10.2      11.1       8.7  \n",
       "1       27.3      23.3      53.7      40.0      40.7      31.5  \n",
       "2       31.5      25.3      45.5      39.6      42.2      32.1  \n",
       "3       39.9      30.4      43.3      36.9      44.6      33.9  \n",
       "4       49.8      36.6      44.7      30.6      49.2      35.8  \n",
       "5       63.6      49.1      52.3      22.7      50.3      28.9  \n",
       "6       81.5      66.4      62.1      19.6      49.7      20.9  \n",
       "7       61.8      51.6      51.5      19.0      37.5      21.0  \n",
       "8       47.6      39.9      44.5      24.7      30.1      24.6  \n",
       "9      103.2     103.6      61.0     276.2     237.6     350.9  \n",
       "10       0.0       0.0       0.0      16.6      15.4       9.7  \n",
       "11       0.0       0.0       0.0      11.0       9.7       6.1  \n",
       "12       0.0       0.0       0.0       6.4       5.2       3.1  \n",
       "13       0.0       0.0       0.0       5.1       4.1       2.5  \n",
       "14       0.0       0.0       0.0       0.9       0.0       0.0  \n",
       "15       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "16       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "17       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "18       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "19       0.0       0.0       0.0       0.0       0.0       0.2  \n",
       "\n",
       "[20 rows x 1382 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ef1.info()\n",
    "ef1.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ef1.to_csv(\"cleaned_MonthlyPrecipitation_Jan1900_to_Dec2014.csv\", index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Jan 1900</th>\n",
       "      <th>Feb 1900</th>\n",
       "      <th>Mar 1900</th>\n",
       "      <th>Apr 1900</th>\n",
       "      <th>May 1900</th>\n",
       "      <th>Jun 1900</th>\n",
       "      <th>Jul 1900</th>\n",
       "      <th>Aug 1900</th>\n",
       "      <th>...</th>\n",
       "      <th>Mar 2014</th>\n",
       "      <th>Apr 2014</th>\n",
       "      <th>May 2014</th>\n",
       "      <th>Jun 2014</th>\n",
       "      <th>Jul 2014</th>\n",
       "      <th>Aug 2014</th>\n",
       "      <th>Sep 2014</th>\n",
       "      <th>Oct 2014</th>\n",
       "      <th>Nov 2014</th>\n",
       "      <th>Dec 2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "      <td>85794.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.436540</td>\n",
       "      <td>1.060109</td>\n",
       "      <td>48.431712</td>\n",
       "      <td>43.624701</td>\n",
       "      <td>41.093362</td>\n",
       "      <td>42.824277</td>\n",
       "      <td>38.047119</td>\n",
       "      <td>51.056387</td>\n",
       "      <td>52.437842</td>\n",
       "      <td>54.547067</td>\n",
       "      <td>...</td>\n",
       "      <td>41.871928</td>\n",
       "      <td>37.572697</td>\n",
       "      <td>43.594364</td>\n",
       "      <td>44.885814</td>\n",
       "      <td>48.544768</td>\n",
       "      <td>54.003677</td>\n",
       "      <td>45.465838</td>\n",
       "      <td>42.516427</td>\n",
       "      <td>41.089695</td>\n",
       "      <td>40.504176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>88.008337</td>\n",
       "      <td>57.848430</td>\n",
       "      <td>72.010699</td>\n",
       "      <td>60.525697</td>\n",
       "      <td>66.856413</td>\n",
       "      <td>67.345912</td>\n",
       "      <td>63.442895</td>\n",
       "      <td>80.688875</td>\n",
       "      <td>89.439978</td>\n",
       "      <td>77.325112</td>\n",
       "      <td>...</td>\n",
       "      <td>73.325001</td>\n",
       "      <td>63.570966</td>\n",
       "      <td>68.773891</td>\n",
       "      <td>75.063681</td>\n",
       "      <td>82.557595</td>\n",
       "      <td>81.385121</td>\n",
       "      <td>70.543566</td>\n",
       "      <td>62.302927</td>\n",
       "      <td>64.250201</td>\n",
       "      <td>72.311727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-179.750000</td>\n",
       "      <td>-89.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-62.250000</td>\n",
       "      <td>-71.750000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.250000</td>\n",
       "      <td>19.250000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>30.300000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>15.800000</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>12.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>93.750000</td>\n",
       "      <td>52.250000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>51.300000</td>\n",
       "      <td>50.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>43.300000</td>\n",
       "      <td>59.200000</td>\n",
       "      <td>71.400000</td>\n",
       "      <td>70.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>43.175000</td>\n",
       "      <td>40.400000</td>\n",
       "      <td>51.700000</td>\n",
       "      <td>59.100000</td>\n",
       "      <td>65.300000</td>\n",
       "      <td>73.200000</td>\n",
       "      <td>57.600000</td>\n",
       "      <td>55.700000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>44.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179.750000</td>\n",
       "      <td>83.250000</td>\n",
       "      <td>1003.200000</td>\n",
       "      <td>780.900000</td>\n",
       "      <td>742.100000</td>\n",
       "      <td>1633.700000</td>\n",
       "      <td>1107.800000</td>\n",
       "      <td>2661.900000</td>\n",
       "      <td>3213.700000</td>\n",
       "      <td>1347.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>926.600000</td>\n",
       "      <td>735.800000</td>\n",
       "      <td>1488.200000</td>\n",
       "      <td>2190.400000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>2678.300000</td>\n",
       "      <td>1712.900000</td>\n",
       "      <td>889.500000</td>\n",
       "      <td>718.600000</td>\n",
       "      <td>1738.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 1382 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Longitude      Latitude      Jan 1900      Feb 1900      Mar 1900  \\\n",
       "count  85794.000000  85794.000000  85794.000000  85794.000000  85794.000000   \n",
       "mean      18.436540      1.060109     48.431712     43.624701     41.093362   \n",
       "std       88.008337     57.848430     72.010699     60.525697     66.856413   \n",
       "min     -179.750000    -89.750000      0.000000      0.000000      0.000000   \n",
       "25%      -62.250000    -71.750000      7.300000      5.600000      0.000000   \n",
       "50%       28.250000     19.250000     26.000000     23.600000     14.300000   \n",
       "75%       93.750000     52.250000     54.000000     51.300000     50.300000   \n",
       "max      179.750000     83.250000   1003.200000    780.900000    742.100000   \n",
       "\n",
       "           Apr 1900      May 1900      Jun 1900      Jul 1900      Aug 1900  \\\n",
       "count  85794.000000  85794.000000  85794.000000  85794.000000  85794.000000   \n",
       "mean      42.824277     38.047119     51.056387     52.437842     54.547067   \n",
       "std       67.345912     63.442895     80.688875     89.439978     77.325112   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        3.000000      0.000000      5.800000      0.000000     11.700000   \n",
       "50%       17.600000     16.000000     30.300000     19.100000     30.400000   \n",
       "75%       47.800000     43.300000     59.200000     71.400000     70.600000   \n",
       "max     1633.700000   1107.800000   2661.900000   3213.700000   1347.300000   \n",
       "\n",
       "       ...      Mar 2014      Apr 2014      May 2014      Jun 2014  \\\n",
       "count  ...  85794.000000  85794.000000  85794.000000  85794.000000   \n",
       "mean   ...     41.871928     37.572697     43.594364     44.885814   \n",
       "std    ...     73.325001     63.570966     68.773891     75.063681   \n",
       "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    ...      1.600000      1.100000      2.900000      0.400000   \n",
       "50%    ...     14.000000     13.400000     20.400000     15.800000   \n",
       "75%    ...     43.175000     40.400000     51.700000     59.100000   \n",
       "max    ...    926.600000    735.800000   1488.200000   2190.400000   \n",
       "\n",
       "           Jul 2014      Aug 2014      Sep 2014      Oct 2014      Nov 2014  \\\n",
       "count  85794.000000  85794.000000  85794.000000  85794.000000  85794.000000   \n",
       "mean      48.544768     54.003677     45.465838     42.516427     41.089695   \n",
       "std       82.557595     81.385121     70.543566     62.302927     64.250201   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.100000      1.100000      0.800000      2.500000      2.200000   \n",
       "50%       17.800000     28.100000     20.800000     20.400000     17.200000   \n",
       "75%       65.300000     73.200000     57.600000     55.700000     48.200000   \n",
       "max     2004.000000   2678.300000   1712.900000    889.500000    718.600000   \n",
       "\n",
       "           Dec 2014  \n",
       "count  85794.000000  \n",
       "mean      40.504176  \n",
       "std       72.311727  \n",
       "min        0.000000  \n",
       "25%        0.900000  \n",
       "50%       12.600000  \n",
       "75%       44.100000  \n",
       "max     1738.800000  \n",
       "\n",
       "[8 rows x 1382 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ef1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA with our Data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's plot the trends in mean monthly Precipitation in the year 1900 vs the mean of all years vs the year 2014 \n",
    "\n",
    "\n",
    "new1 = ef1.describe()\n",
    "new1 = new1.astype(float)\n",
    "listMonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
    "for m in listMonths:\n",
    "    listY = []\n",
    "    # List of January months\n",
    "    for x in range(1900,2015):\n",
    "        yearS = str(x)\n",
    "        month = m+ \" \" + yearS\n",
    "        listY.append(month)\n",
    "\n",
    "    # print(listY)\n",
    "    # new1.convert_objects(convert_numeric=True).head()\n",
    "\n",
    "    # Add mean columns of January\n",
    "    new1[\"Avg\"+m] = new1[listY].mean(axis=1)\n",
    "\n",
    "#     new1[\"AvgJan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[42.25716171496803,\n",
       " 39.798554261928366,\n",
       " 44.54618102411183,\n",
       " 41.08075519621867,\n",
       " 44.487637840287086,\n",
       " 47.59720119274637,\n",
       " 54.73063843524126,\n",
       " 54.23993534563627,\n",
       " 47.70267724204953,\n",
       " 45.07384985876216,\n",
       " 42.42358377144084,\n",
       " 42.42554916681169]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing out mean monthly precipitation of mean of all years\n",
    "meanPrecip = []\n",
    "for m in listMonths:\n",
    "    meanPrecip.append(new1[\"Avg\"+m][\"mean\"])\n",
    "\n",
    "meanPrecip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[48.43171200783357,\n",
       " 43.62470102804501,\n",
       " 41.0933620066675,\n",
       " 42.824276755950514,\n",
       " 38.047118679627914,\n",
       " 51.056387393058245,\n",
       " 52.43784180711939,\n",
       " 54.547067393990176,\n",
       " 46.45499102501364,\n",
       " 43.18918805510856,\n",
       " 41.580483483693506,\n",
       " 45.19333286709998]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Printing out mean monthly precipitation of year 1900\n",
    "Precip1900 = []\n",
    "for m in listMonths:\n",
    "    Precip1900.append(new1[m+\" 1900\"][\"mean\"])\n",
    "\n",
    "Precip1900"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[41.00000699349686,\n",
       " 38.42854045737573,\n",
       " 41.87192810686132,\n",
       " 37.57269739142694,\n",
       " 43.59436440776849,\n",
       " 44.88581369326578,\n",
       " 48.54476769937399,\n",
       " 54.003677413340164,\n",
       " 45.46583793738552,\n",
       " 42.51642655663561,\n",
       " 41.08969508357368,\n",
       " 40.504176282725794]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Printing out mean monthly precipitation of year 2014\n",
    "Precip2014 = []\n",
    "for m in listMonths:\n",
    "    Precip2014.append(new1[m+\" 2014\"][\"mean\"])\n",
    "\n",
    "Precip2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the mean monthly Precipitation in the year 1900 vs the mean of all years vs the year 2014 \n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.title(\"Precipitation Comparison: Mean, 1900, 2014\")\n",
    "\n",
    "plt.plot(listMonths,Precip1900, label=\"Year 1900\",marker='o',markerfacecolor='darkred',markersize=12,color='lightcoral',\n",
    "         linewidth=4)\n",
    "\n",
    "plt.plot(listMonths,meanPrecip, label=\"Mean\",marker='',color='yellowgreen',\n",
    "         linewidth=2)\n",
    "\n",
    "plt.plot(listMonths,Precip2014, label=\"Year 2014\",marker='',color='cyan',\n",
    "         linewidth=2, linestyle = \"dashed\")\n",
    "\n",
    "# plt.label(\"Year 1900\")\n",
    "plt.legend()\n",
    "fig1 = plt.gcf()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig1.savefig('precippppp.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's plot the trends in mean monthly Temperature in the year 1900 vs the mean of all years vs the year 2014 \n",
    "\n",
    "\n",
    "new2 = df1.describe()\n",
    "new2 = new2.astype(float)\n",
    "listMonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
    "for m in listMonths:\n",
    "    listY = []\n",
    "    # List of January months\n",
    "    for x in range(1900,2015):\n",
    "        yearS = str(x)\n",
    "        month = m+ \" \" + yearS\n",
    "        listY.append(month)\n",
    "\n",
    "    # print(listY)\n",
    "    # new1.convert_objects(convert_numeric=True).head()\n",
    "\n",
    "    # Add mean columns of January\n",
    "    new2[\"Avg\"+m] = new2[listY].mean(axis=1)\n",
    "\n",
    "#     new1[\"AvgJan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-7.436555348453474, -9.073614634042505, -9.1744681648965, -6.910142981519938, -3.9133071736039082, -1.551567201922502, -0.6835402698678623, -1.4546657666341318, -3.102605198904147, -4.666632844498097, -5.694412237199119, -6.320863189986943]\n",
      "[-8.415361214070925, -9.0448714362311, -8.795384292607785, -6.884104949064119, -3.7499475487796783, -1.5428969391798921, -1.2465219012984567, -1.6077895890155576, -2.9789390866494023, -4.2512215306431225, -5.945491526213908, -6.309593910996009]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[-6.855600624752311,\n",
       " -9.36664218943026,\n",
       " -8.996130265519659,\n",
       " -5.943503042170706,\n",
       " -2.4533627060167467,\n",
       " -0.8774891018019868,\n",
       " -0.8037356924726528,\n",
       " -0.6801419679697651,\n",
       " -2.226570622654268,\n",
       " -4.361294496118648,\n",
       " -5.511663985826508,\n",
       " -5.637958365386899]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing out mean monthly temperature of mean of all years\n",
    "meanTemp = []\n",
    "for m in listMonths:\n",
    "    meanTemp.append(new2[\"Avg\"+m][\"mean\"])\n",
    "\n",
    "print (meanTemp)\n",
    "\n",
    "# Printing out mean monthly temperature of year 1900\n",
    "Temp1900 = []\n",
    "for m in listMonths:\n",
    "    Temp1900.append(new2[m+\" 1900\"][\"mean\"])\n",
    "\n",
    "print(Temp1900)\n",
    "\n",
    "# Printing out mean monthly temperature of year 2014\n",
    "Temp2014 = []\n",
    "for m in listMonths:\n",
    "    Temp2014.append(new2[m+\" 2014\"][\"mean\"])\n",
    "\n",
    "Temp2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the mean monthly Temperature in the year 1900 vs the mean of all years vs the year 2014 \n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.title(\"Temperature Comparison: Mean, 1900, 2014\")\n",
    "\n",
    "plt.plot(listMonths,Temp1900, label=\"Year 1900\",marker='o',markerfacecolor='olivedrab',markersize=12,color='yellowgreen',\n",
    "         linewidth=4)\n",
    "\n",
    "plt.plot(listMonths,meanTemp, label=\"Mean\",marker='',color='cyan',\n",
    "         linewidth=2)\n",
    "\n",
    "plt.plot(listMonths,Temp2014, label=\"Year 2014\",marker='',color='hotpink',\n",
    "         linewidth=2, linestyle = \"dashed\")\n",
    "\n",
    "# plt.label(\"Year 1900\")\n",
    "plt.legend()\n",
    "fig2 = plt.gcf()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "fig2.savefig('tempppppp.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executing our model using Linear Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.906886</td>\n",
       "      <td>11.710341</td>\n",
       "      <td>1.247260e+01</td>\n",
       "      <td>4.905435</td>\n",
       "      <td>3.059161</td>\n",
       "      <td>5.958738e+00</td>\n",
       "      <td>1.024784e+01</td>\n",
       "      <td>1.200584e+01</td>\n",
       "      <td>1.559985e+01</td>\n",
       "      <td>1.147546e+01</td>\n",
       "      <td>10.228974</td>\n",
       "      <td>8.500942e+00</td>\n",
       "      <td>10.339339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.772241</td>\n",
       "      <td>22.034911</td>\n",
       "      <td>1.751727e+01</td>\n",
       "      <td>9.456831</td>\n",
       "      <td>23.178640</td>\n",
       "      <td>1.534989e+01</td>\n",
       "      <td>2.868153e+01</td>\n",
       "      <td>3.378672e+01</td>\n",
       "      <td>4.942416e+01</td>\n",
       "      <td>4.055705e+01</td>\n",
       "      <td>30.885323</td>\n",
       "      <td>2.619417e+01</td>\n",
       "      <td>27.069895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.057248</td>\n",
       "      <td>25.427971</td>\n",
       "      <td>1.857660e+01</td>\n",
       "      <td>9.217719</td>\n",
       "      <td>29.491328</td>\n",
       "      <td>1.529094e+01</td>\n",
       "      <td>3.255547e+01</td>\n",
       "      <td>3.415742e+01</td>\n",
       "      <td>4.155990e+01</td>\n",
       "      <td>4.043054e+01</td>\n",
       "      <td>31.840381</td>\n",
       "      <td>2.742965e+01</td>\n",
       "      <td>27.836263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.724294</td>\n",
       "      <td>31.681006</td>\n",
       "      <td>2.110698e+01</td>\n",
       "      <td>10.539274</td>\n",
       "      <td>37.674670</td>\n",
       "      <td>1.769305e+01</td>\n",
       "      <td>4.213071e+01</td>\n",
       "      <td>3.787658e+01</td>\n",
       "      <td>3.814262e+01</td>\n",
       "      <td>3.952282e+01</td>\n",
       "      <td>34.343405</td>\n",
       "      <td>3.151094e+01</td>\n",
       "      <td>31.078863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.887307</td>\n",
       "      <td>38.706987</td>\n",
       "      <td>2.434751e+01</td>\n",
       "      <td>12.559512</td>\n",
       "      <td>42.270961</td>\n",
       "      <td>2.022752e+01</td>\n",
       "      <td>5.418735e+01</td>\n",
       "      <td>4.210935e+01</td>\n",
       "      <td>3.707040e+01</td>\n",
       "      <td>3.738716e+01</td>\n",
       "      <td>39.224153</td>\n",
       "      <td>3.759461e+01</td>\n",
       "      <td>34.964401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85789</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.009071</td>\n",
       "      <td>2.372787e-07</td>\n",
       "      <td>0.010156</td>\n",
       "      <td>0.022266</td>\n",
       "      <td>3.439163e-09</td>\n",
       "      <td>2.270498e-07</td>\n",
       "      <td>9.894113e-10</td>\n",
       "      <td>3.434279e-09</td>\n",
       "      <td>2.443962e-05</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>2.502983e-07</td>\n",
       "      <td>0.007652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85790</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.006238</td>\n",
       "      <td>2.258466e-07</td>\n",
       "      <td>0.009570</td>\n",
       "      <td>0.021680</td>\n",
       "      <td>2.372254e-09</td>\n",
       "      <td>1.434443e-07</td>\n",
       "      <td>7.513912e-10</td>\n",
       "      <td>2.667238e-09</td>\n",
       "      <td>1.224385e-05</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>3.126629e-03</td>\n",
       "      <td>0.011744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85791</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.004395</td>\n",
       "      <td>3.908398e-04</td>\n",
       "      <td>0.009180</td>\n",
       "      <td>0.021289</td>\n",
       "      <td>1.491885e-09</td>\n",
       "      <td>8.368202e-08</td>\n",
       "      <td>5.254227e-10</td>\n",
       "      <td>1.902378e-09</td>\n",
       "      <td>4.835544e-08</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>1.562958e-02</td>\n",
       "      <td>0.012598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85792</th>\n",
       "      <td>0.100098</td>\n",
       "      <td>0.002478</td>\n",
       "      <td>3.125206e-03</td>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.021289</td>\n",
       "      <td>8.120668e-10</td>\n",
       "      <td>4.784343e-08</td>\n",
       "      <td>2.965450e-10</td>\n",
       "      <td>1.144090e-09</td>\n",
       "      <td>4.806924e-08</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>1.850785e-01</td>\n",
       "      <td>0.026746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85793</th>\n",
       "      <td>0.100902</td>\n",
       "      <td>0.002246</td>\n",
       "      <td>6.250100e-03</td>\n",
       "      <td>0.008008</td>\n",
       "      <td>0.021289</td>\n",
       "      <td>2.587613e-10</td>\n",
       "      <td>1.197063e-08</td>\n",
       "      <td>1.055027e-10</td>\n",
       "      <td>3.799821e-10</td>\n",
       "      <td>1.201745e-08</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>2.999051e-01</td>\n",
       "      <td>0.036566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85794 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Jan        Feb           Mar        Apr        May           Jun  \\\n",
       "0      17.906886  11.710341  1.247260e+01   4.905435   3.059161  5.958738e+00   \n",
       "1      27.772241  22.034911  1.751727e+01   9.456831  23.178640  1.534989e+01   \n",
       "2      28.057248  25.427971  1.857660e+01   9.217719  29.491328  1.529094e+01   \n",
       "3      30.724294  31.681006  2.110698e+01  10.539274  37.674670  1.769305e+01   \n",
       "4      33.887307  38.706987  2.434751e+01  12.559512  42.270961  2.022752e+01   \n",
       "...          ...        ...           ...        ...        ...           ...   \n",
       "85789   0.050000   0.009071  2.372787e-07   0.010156   0.022266  3.439163e-09   \n",
       "85790   0.100000   0.006238  2.258466e-07   0.009570   0.021680  2.372254e-09   \n",
       "85791   0.100000   0.004395  3.908398e-04   0.009180   0.021289  1.491885e-09   \n",
       "85792   0.100098   0.002478  3.125206e-03   0.008594   0.021289  8.120668e-10   \n",
       "85793   0.100902   0.002246  6.250100e-03   0.008008   0.021289  2.587613e-10   \n",
       "\n",
       "                Jul           Aug           Sep           Oct        Nov  \\\n",
       "0      1.024784e+01  1.200584e+01  1.559985e+01  1.147546e+01  10.228974   \n",
       "1      2.868153e+01  3.378672e+01  4.942416e+01  4.055705e+01  30.885323   \n",
       "2      3.255547e+01  3.415742e+01  4.155990e+01  4.043054e+01  31.840381   \n",
       "3      4.213071e+01  3.787658e+01  3.814262e+01  3.952282e+01  34.343405   \n",
       "4      5.418735e+01  4.210935e+01  3.707040e+01  3.738716e+01  39.224153   \n",
       "...             ...           ...           ...           ...        ...   \n",
       "85789  2.270498e-07  9.894113e-10  3.434279e-09  2.443962e-05   0.000303   \n",
       "85790  1.434443e-07  7.513912e-10  2.667238e-09  1.224385e-05   0.000297   \n",
       "85791  8.368202e-08  5.254227e-10  1.902378e-09  4.835544e-08   0.000295   \n",
       "85792  4.784343e-08  2.965450e-10  1.144090e-09  4.806924e-08   0.000294   \n",
       "85793  1.197063e-08  1.055027e-10  3.799821e-10  1.201745e-08   0.000196   \n",
       "\n",
       "                Dec       mean  \n",
       "0      8.500942e+00  10.339339  \n",
       "1      2.619417e+01  27.069895  \n",
       "2      2.742965e+01  27.836263  \n",
       "3      3.151094e+01  31.078863  \n",
       "4      3.759461e+01  34.964401  \n",
       "...             ...        ...  \n",
       "85789  2.502983e-07   0.007652  \n",
       "85790  3.126629e-03   0.011744  \n",
       "85791  1.562958e-02   0.012598  \n",
       "85792  1.850785e-01   0.026746  \n",
       "85793  2.999051e-01   0.036566  \n",
       "\n",
       "[85794 rows x 13 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean annual precipitation for every location\n",
    "\n",
    "dfPrecip['mean'] = dfPrecip.mean(axis=1)\n",
    "dfPrecip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding best train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 1775.05\n",
      "Mean squared error: 1808.93\n",
      "Mean squared error: 1751.75\n",
      "Mean squared error: 1817.26\n",
      "Mean squared error: 1770.22\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "mse = []\n",
    "possible_splits = [0.10,0.15,0.20,0.25,0.30]\n",
    "for sz in possible_splits:\n",
    "    # Finding the best train-test split\n",
    "    X_train, X_test, y_train, y_test = train_test_split(dfTemp, dfPrecip['mean'], test_size=sz)\n",
    "\n",
    "    # Linear Regression\n",
    "    # Create linear regression object\n",
    "    regr = linear_model.LinearRegression()\n",
    "\n",
    "    # Train the model using the training sets\n",
    "    regr.fit(X_train, y_train)\n",
    "    \n",
    "    my_pred = regr.predict(X_test)\n",
    "\n",
    "    # The mean squared error\n",
    "    current_mse = mean_squared_error(y_test, my_pred)\n",
    "    mse.append(current_mse)\n",
    "    print('Mean squared error: %.2f'\n",
    "          % current_mse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best test size for train-test split is 0.2\n"
     ]
    }
   ],
   "source": [
    "# Finding the best split\n",
    "n = np.argmin(mse)\n",
    "\n",
    "print ('The best test size for train-test split is', possible_splits[n])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training our model with the 80-20 split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(dfTemp, dfPrecip['mean'], test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Right now our dataset looks like this: \n",
    "#        X = X_train   + X_test \n",
    "# 80K rows = 60k rows + 20k rows \n",
    "\n",
    "#        y = y_train   + y_test \n",
    "# 80K rows = 60k rows + 20k rows  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets, linear_model\n",
    "\n",
    "# Linear Regression\n",
    "# Create linear regression object\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediciting on our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_pred = regr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [-2.28791131  4.57552108 -2.26940253  1.89933999 -1.58942057 -0.76093129\n",
      "  3.0483552   1.00016227 -3.03739748  0.06332089 -1.73389818  2.45792963]\n",
      "Mean squared error: 1785.76\n",
      "Coefficient of determination: 0.34\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# The coefficients\n",
    "print('Coefficients: \\n', regr.coef_)\n",
    "# The mean squared error\n",
    "print('Mean squared error: %.2f'\n",
    "      % mean_squared_error(y_test, my_pred))\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print('Coefficient of determination: %.2f'\n",
    "      % r2_score(y_test, my_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   mean   R-squared:                       0.342\n",
      "Model:                            OLS   Adj. R-squared:                  0.342\n",
      "Method:                 Least Squares   F-statistic:                     3714.\n",
      "Date:                Tue, 27 Apr 2021   Prob (F-statistic):               0.00\n",
      "Time:                        18:49:47   Log-Likelihood:            -4.4355e+05\n",
      "No. Observations:               85794   AIC:                         8.871e+05\n",
      "Df Residuals:                   85781   BIC:                         8.873e+05\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         56.9556      0.293    194.397      0.000      56.381      57.530\n",
      "Jan           -2.2737      0.097    -23.553      0.000      -2.463      -2.085\n",
      "Feb            4.5432      0.111     41.049      0.000       4.326       4.760\n",
      "Mar           -2.2279      0.125    -17.781      0.000      -2.473      -1.982\n",
      "Apr            1.8786      0.127     14.841      0.000       1.631       2.127\n",
      "May           -1.5960      0.119    -13.373      0.000      -1.830      -1.362\n",
      "Jun           -0.7114      0.136     -5.239      0.000      -0.978      -0.445\n",
      "Jul            2.9740      0.143     20.756      0.000       2.693       3.255\n",
      "Aug            0.9925      0.141      7.035      0.000       0.716       1.269\n",
      "Sep           -2.9911      0.177    -16.852      0.000      -3.339      -2.643\n",
      "Oct            0.0591      0.136      0.434      0.665      -0.208       0.326\n",
      "Nov           -1.6936      0.112    -15.138      0.000      -1.913      -1.474\n",
      "Dec            2.4070      0.097     24.819      0.000       2.217       2.597\n",
      "==============================================================================\n",
      "Omnibus:                    39683.307   Durbin-Watson:                   0.199\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           466974.787\n",
      "Skew:                           1.923   Prob(JB):                         0.00\n",
      "Kurtosis:                      13.763   Cond. No.                         198.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "\n",
    "\n",
    "diabetes = datasets.load_diabetes()\n",
    "X = dfTemp\n",
    "y = dfPrecip['mean']\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:** We see that only the mean temperatures in February, April, July, August, and December have an impact on the mean precipitation. This is because the t-values of these variables are considerably bigger than 0. The mean squared error on this model is also the least among all the other models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thus our Equation for Linear regression model looks like this:\n",
    "(without including random error)\n",
    "<pre>\n",
    "**y = -2.2737 * X1 + 4.5432 * X2 - 2.2279 * X3 + 1.8786 * X4  - 1.5960 * X5  - 0.7114 * X6  + <br>\n",
    "       2.9740 * X7 + 0.9925 * X8 - 2.9911 * X9 + 0.0591 * X10 - 1.6936 * X11 + 2.4070 * X12 + 56.9556** </pre>\n",
    "     \n",
    "Here, <br>\n",
    "<br>\n",
    "**y** = Annual mean precipitation<br>\n",
    "**X1** = Monthly mean temperature in January<br>\n",
    "**X2** = Monthly mean temperature in February<br>\n",
    "**X3** = Monthly mean temperature in March<br>\n",
    "**X4** = Monthly mean temperature in April<br>\n",
    "**X5** = Monthly mean temperature in May<br>\n",
    "**X6** = Monthly mean temperature in June<br>\n",
    "**X7** = Monthly mean temperature in July<br>\n",
    "**X8** = Monthly mean temperature in August<br>\n",
    "**X9** = Monthly mean temperature in Sepetember<br>\n",
    "**X10** = Monthly mean temperature in October<br>\n",
    "**X11** = Monthly mean temperature in November<br>\n",
    "**X12** = Monthly mean temperature in December<br>\n",
    "     \n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting our linear regression for each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting our linear regression for every feature\n",
    "# Reference: https://stackoverflow.com/a/52464283\n",
    "\n",
    "train_copy =pd.get_dummies(X_train)\n",
    "train_copy=train_copy.fillna(0)\n",
    "clf = linear_model.LinearRegression()\n",
    "\n",
    "fig, axes = plt.subplots(1,len(train_copy.columns.values),sharey=True,constrained_layout=True,figsize=(30,15))\n",
    "\n",
    "for i,e in enumerate(train_copy.columns):\n",
    "  clf.fit(train_copy[e].values[:,np.newaxis], y_train)\n",
    "  axes[i].set_title(\"Best fit line\")\n",
    "  axes[i].set_xlabel(str(e))\n",
    "  axes[i].set_ylabel('Precipitation (in mm)')\n",
    "  axes[i].scatter(train_copy[e].values[:,np.newaxis], y_train,color='g')\n",
    "  axes[i].plot(train_copy[e].values[:,np.newaxis], \n",
    "  clf.predict(train_copy[e].values[:,np.newaxis]),color='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:** We see that as the Temperature increases, our linear regression model faces more outliers, which is what accounts for more errors for the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting our predicted y and actual test y to see how they coincide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17159, 12)\n",
      "(17159,)\n",
      "(17159,)\n",
      "17159\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting our predicted y and actual test y to see how they coincide\n",
    "plt.figure(figsize=(10, 10))\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "print(my_pred.shape)\n",
    "\n",
    "myListMonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
    "myEx = list(range(1,17160))\n",
    "\n",
    "print(len(myEx))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(myEx, y_test,  color='black')\n",
    "plt.scatter(myEx, my_pred, color='blue',marker='+')\n",
    "plt.xlabel('Locations')\n",
    "plt.ylabel('Y (Precipitation in mm)')\n",
    "plt.title('Plotting predicted y vs correct y for all locations')\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:** We do see that values in the lower range are better predicted. Let's take only the first 100 values for a better view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17159, 12)\n",
      "(17159,)\n",
      "(17159,)\n",
      "17159\n"
     ]
    },
    {
     "data": {
      "image/png": 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BAAAEEJQAAAACCEoAAAABBCUAAIAAghIAAEAAQQkAACCAoATUVL/fV6fTUaPRUKfTUb/fz7tIAFA4J+ZdAADZ6/f7mp+f19ramiRpZWVF8/PzkqRut5tn0QCgUKhRAmpoYWHhWEgaWFtb08LCQk4lAoBiIigBNbS6ujrReACoK4ISUEOtVmui8QBQVwQloIZ6vZ6azeaGcc1mU71eL6cSAUAxEZSAGup2u1pcXFS73ZaZqd1ua3FxkYbcALCJufvMJzo3N+dLS0szny4AAMCsmdkBd58b9R41SgAAAAEEJQAAgACCEgAAQABBCQAAIICgBAAAEEBQAgAACCAoAQAABBCUAAAAAghKAAAAAQQlAACAAIISAABAAEEJAAAggKAEAAAQQFACAAAIICgBAAAEEJQAAAACCEoAAAABBCUAAIAAghIAAEAAQQkAACCAoAQAABBAUAIAAAggKAEAAAQQlAAAAAIISgAAAAEEJQAAgACCEgAAQABBCQAAIICgBAAAEEBQAgAACCAoAQAABBCUAAAAAghKAAAAAQQlAACAAIISAABAAEEJAAAggKAEAAAQQFACAAAIICgBAAAEEJQAAAACCEoAAAABBCUAAIAAghIAAEAAQQkAACCAoAQAABBAUAIAAAggKAEAAAQQlAAAAAIISgAAAAEEJQAAgACCEgAAQABBCQAAIICgBAAAEEBQAgAACCAoAQAABBCUAAAAAghKAAAAAQQlAACAAIISAABAAEEJAAAggKAEAAAQQFACAAAIICgBAAAEEJQAAAACCEoAAAABBCUAAIAAghIAAEAAQQkAACCAoAQAABBAUAIAAAggKAEAAAQQlAAAAAIISgAAAAEEJQAAgACCEgAAQABBCQAAIICgBAAAEEBQAgAACCAoAQAABBCUAAAAAghKAAAAAQQlAACAAIISAABAAEEJAAAggKAEAAAQQFACAAAIICgBAAAEEJQAAAACCEoAAAABBCUAAIAAghIAAEAAQQkAACCAoAQAABBAUAIAAAggKAEAAAQQlAAAAAIISgAAAAEEJQAAgACCEgAAQABBCQAAIICgBAAAEEBQAgAACCAoAQAABBCUAAAAAghKAAAAAQQlAACAAIISAABAAEEJAAAggKAEAAAQQFACAAAIICgBAAAEEJQAAAACCEoAAAABBCUAAIAAghIAAEAAQQkAACCAoAQAABBAUAIAAAggKAEAAAQQlAAAAAIISgAAlEy/31en01Gj0VCn01G/38+7SJV1Yt4FAAAAyfX7fc3Pz2ttbU2StLKyovn5eUlSt9vNs2iVRI0SAAAlsrCwcCwkDaytrWlhYSGnElUbQQkAgBJZXV2daDymQ1ACAKBEWq3WROMxHYISAAAl0uv11Gw2N4xrNpvq9Xo5lajaCEoAAJRIt9vV4uKi2u22zEztdluLi4s05E6JufvMJzo3N+dLS0szny4AAMCsmdkBd58b9R41SgAAAAEEJQAAgACCEgAAQABBCQAAIICgBAAAEEBQAgAACCAoAQAABBCUAAAAAghKAAAAAQQlAACAAIISAABAAEEJAAAggKAEAAAQQFACAAAIICgBAAAEEJQAAAACCEoAAAABBCUAAIAAghIAAEAAQQkAgALr9/vqdDpqNBrqdDrq9/t5F6lWTsy7AAAAYLR+v6/5+Xmtra1JklZWVjQ/Py9J6na7eRatNqhRAgCgoBYWFo6FpIG1tTUtLCzkVKL6ISgBAFBQq6urE43H7BGUAAAoqFarNdF4zB5BCQCAgur1emo2mxvGNZtN9Xq9nEpUPwQlAAAKqtvtanFxUe12W2amdrutxcVFGnJnyNx95hOdm5vzpaWlmU8XAABg1szsgLvPjXqPGiUAAIAAghIAAEAAQQkAACCAoAQAABBAUAIAAAggKAEAAAQQlAAAAAIISgAAAAEEJSAF/X5fnU5HjUZDnU5H/X4/7yIBALbhxLwLAFRNv9/X/Py81tbWJEkrKyuan5+XJB47AAAlQ40SMGMLCwvHQtLA2tqaFhYWcioRAGC7CErAjK2urk40HgBQXAQlYMZardZE4wEAxUVQAmas1+up2WxuGNdsNtXr9XIqEQBguwhKwIx1u10tLi6q3W7LzNRut7W4uEhDbgAoIXP3mU90bm7Ol5aWZj5dAACAWTOzA+4+N+o9apQAAAACCEoAAAABBCUAAIAAghIAAEAAQQkAACCAoAQAABBAUAIAAAggKAEAAAQQlAAAAAIISgAAAAEEJQAAgACCEgAAQABBCQAAIICgBAAAEEBQAgAACDhxqw+YWUPSgyXdU9Jtkj7v7gfTLhgAAEDegkHJzO4r6dckPUbSFyXdJOkUSfc3szVJb5F0mbsfzaKgAAAAWRtXo/Q7kv5E0gvc3YffMLN7SHqGpGdJuiy94gEAAOQnGJTc/elj3vuqpD9Io0AAAABFkaSN0gmSniCpM/x5d39DesUCAADI35ZBSdIHJB2W9FlJtEcCAAC1kSQo3cvdfzD1kgAAABRMkn6UPmxmP556SQAAAAomSVDaL+kKM7vNzA6Z2S1mdijtggFAUv1+X51OR41GQ51OR/1+P+8iAblgW5i9JJfeXi/p4ZI+u7mbAADIW7/f1/z8vNbW1iRJKysrmp+flyR1u908iwZkim0hHbZV9jGzj0h63CQdS87NzfnS0tK0ZQOALXU6Ha2srBw3vt1ua3l5OfsCATlhW9g+Mzvg7nOj3ktSo/RlSXvN7MOSjgxG0j0AgCJYXV2daDxQVWwL6UjSRukGSR+TdLKk04cGAMhdq9WaaDxQVWwL6diyRsndX5lFQQBgO3q93oZ2GZLUbDbV6/VyLBWQPbaFdGxZo2Rmc2Z2hZl9ysw+MxiyKBwAbKXb7WpxcVHtdltmpna7rcXFRRqvonbYFtKRpDH3dZJ+VZt65nb341uMxWjMDQAAymLaxtw3uftfzbhMAAAAhZckKL3czP5UUYPu4bve3ptaqQAAAAogSVB6rqRzJZ2k9UtvLomgBAAAKi1JUHqwuz8o9ZIAAAAUTKJnvZnZD6ReEgAAgIJJUqP0KEnPMbMbFLVRMknu7j+YaskAAAByliQoPTb1UgAAABRQkp65g/0lAQAAVFmSNkoAAAC1RFACAAAIICgBAAAEJHko7s+Y2RfN7FtmdsjMbjGzQ1kUDgAAIE9J7np7naQnuvs1aRcGAACgSJJcejtISAIAAHWUpEZpycz+QtL7xENxAQBAjSQJSmdIWpP040PjeCguAACovCQdTj43i4IAAAAUTTAomdlL3f11ZvYmRTVIG7j7f0u1ZAAAADkbV6M0aMC9lEVBAAAAiiYYlNz9A/G/l2VXHAAAgOKgZ24AAIAAghIAAEAAQQkAACBgy+4BzOzuki6R1Bn+vLs/L71iAQAA5C9Jh5Pvl/T/JH1U0nfTLQ4AAEBxJAlKTXf/tdRLAgAAUDBJ2ih90Mwen3pJAAAACiZJUPrvisLSYTO7JR4OpV0wAACAvG0ZlNz9dHdvuPsp8f9Pd/czsigc1vX7fXU6HTUaDXU6HfX7/byLBABA5SVpoyQze5KkR8cv97r7B9MrEjbr9/uan5/X2tqaJGllZUXz8/OSpG63m2fRAACoNHM/7nm3Gz9g9lpJPyxpUIXxdEkH3P3XQ38zNzfnS0s8Im5WOp2OVlZWjhvfbre1vLycfYEAAKgQMzvg7nOj3kvSRunxkn7M3f/c3f9c0mPjccjI6urqROOrjsuQAICsJO2Z+8yh/985hXJgjFarNdH4KhtchlxZWZG7H7sMSVgCAKQhSVD6XUn/bGZvM7PLJB2Q9Jp0i4VhvV5PzWZzw7hms6ler5dTifKzsLBwrK3WwNramhYWFnIqEQCgypLc9fZOSRdKem88PNzd35V2wbCu2+1qcXFR7XZbZqZ2u63FxcVaNuTmMiQAIEvBxtxmdq67X2tmDxn1vrt/KjRRGnMjLTRsBwDM2rjG3OO6B/ifkuYlvX7Eey7pR2ZQNmAivV5vQ1cJUn0vQwIA0hcMSu4+H//3ce5+ePg9Mzsl1VIBAYPLjQsLC1pdXVWr1VKv16vlZUgAQPqS9KP0KXd/yFbjhnHpDQAAlMW2Lr2Z2fdIOlvSqWb2Q5IsfusMSc3Q3wEAAFTFuDZKPyHpYkn3kvSGofG3SPrNFMsEAABQCOPaKF0m6TIze4q7vyfDMgEAABTClg/Fdff3mNkTJD1A0ilD41+VZsEAAADytmWHk2b2ZklPk/RiRe2UniqpnXK5AAAAcpfkESaPcPdnS7rZ3V8p6eGS7p1usQAAAPKXJCjdFv+7Zmb3lHSHpHPSKxIAAEAxbNlGSdIHzexMSb8v6VOKeuX+0zQLBQAAUARJgtLr3P2IpPeY2QcVNeg+vMXfAAAAlF6SS29XD/7j7kfc/VvD4wAAAKqKnrkBAAAC6JkbAAAggJ65AQAAAsZdenumu++R1DGz/7n5fXd/w4g/AwAAqIxxl97uFP97WhYFAQAAKJpxl97eEv/7yuyKAwAAUBxJnvV2HzP7gJndZGZfNbP3m9l9sigckJd+v69Op6NGo6FOp6N+v593kSqDZQugTJJ0OPkOSX8s6afj1z8n6Z2SHpZWoYA89ft9zc/Pa21tTZK0srKi+fl5SVK3282zaKXHsgVQNubu4z9g9gl3f9imcfvd/cLQ38zNzfnS0tKMighkq9PpaGVl5bjx7XZby8vL2ReoQli2AIrIzA64+9yo95LUKF1lZr8u6V2KnvP2NEkfMrO7SpK7f2NmJQUKYHV1daLxSI5lC6BskgSlp8X/vmDT+OcpCk60V0KltFqtkbUerVYrh9JUC8sWQNls2Zjb3c8ZMxCSUDm9Xk/N5san9DSbTfV6vZxKVB0sWwBlM67DyR9x978zs58Z9b67vze9YgH5GTQqXlhY0Orqqlqtlnq9Ho2NZ4BlC6Bsgo25zeyV7v5yM3vriLfd3Z8XmiiNuQEAQFlsqzG3u788/ve5aRUMAACgyJJ0OPkaMztz6PVdzOx3Ui0VAABAAWwZlCQ9zt2/OXjh7jdLenxqJQIAACiIJEHpBDPbMXhhZqdK2jHm8wAAAJWQpB+lPZI+FjfqdkX9J12WaqkAAAAKYMug5O6vM7PPSHqMJJP0anf/SOolAwAAyFmSS2+SdI2kv3b3l0j6f2Z2eoplAgAAJdfv99XpdNRoNNTpdNTv9/Mu0rZsWaNkZpdImpd0V0n3lXS2pDdL+tF0iwYAAMqo3+9rfn5ea2trkqSVlRXNz89LUuk6mE1So/SLkh4p6ZAkufsXJd0jzUIBAIDyWlhYOBaSBtbW1rSwsJBTibYvSVA64u63D16Y2YmKGnUDAAAcZ3V1daLxRZYkKO0zs9+UdKqZ/Zik/yvpA+kWCwAAlFWr1ZpofJElCUq/JukmSZ+V9AJJV0r6rTQLBQAAyqvX66nZbG4Y12w21ev1cirR9o1tzG1mDUmfcfcHSro0myIBAIAyGzTYXlhY0Orqqlqtlnq9XukacktbBCV3P2pm/2JmLXcv34VFAACQi263W8pgtFmSnrm/V9LnzeyTkr49GOnuT0qtVAAAAAWQJCi9MvVSAAAAFFAwKJnZKZJ+QdL9FDXk/jN3/05WBQMAAMjbuLveLpM0pygkPU7S6zMpEQAAQEGMu/T2A+7+IEkysz+T9MlsigQAAFAM42qU7hj8h0tuAACgjsbVKD3YzA7F/zdFPXMfiv/v7n5G6qUDAADIUTAoufsJWRYEAACgaIKX3szstK3+OMlnAAAAympcG6X3m9nrzezRZnanwUgzu4+ZPd/MPiLpsekXEQAAIB/jLr39qJk9XtGDcB9pZneR9B1J10n6kKTnuPtXsikmAABA9rZ61tuVkq7MqCwAAACFMu7SGwAAQK0RlAAAAALG3fV2pZl1MiwLAABAoYyrUXqbpL8xswUzOymj8gAAABTGuLve3m1mH5L025KWzOztko4Ovf+GDMoHAACQm7F3vSl63tu3Je2QdLqGghIAAEDVBYOSmT1W0hsk/ZWkh7j7WmalAgAAKIBxNUoLkp7q7p/PqjAAAABFMq6N0n/JsiAAAABFQz9KAAAAAQQlZK7f76vT6ajRaKjT6ajf7+ddJAAARtrqrjdgpvr9vubn57W2Ft0bsLKyovn5eUlSt9vNs2gAAByHGiVkamFh4VhIGlhbW9PCwkJOJQIAIIyghEytrq5ONB4AgDwRlJCpVqs10XgAAPJEUEKmer2ems3mhnHNZlO9Xi+nEgEAEEZQQqa63a4WFxfVbrdlZmq321pcXKQhNwCgkMzdZz7Rubk5X1pamvl0AQAAZs3MDrj73Kj3qFECAAAIICgBAAAEEJQAAAACCEoAAAABBCUAAEpo9+5oQLoqHZR4+CoAAJhGZR+Ky8NXAQBVNKhF2rdv4+u9e3MoTA1UtkaJh68CAIBpVbZGqS4PX+VMAgDqZbC/Z/+fjcrWKPHwVQAAMK3K1ij1er0NbZSkaj18lWvUAFBv7O+zUdmgNGiw/fzn31dHjhxWu32xer0eDbkBAEBilX8obtVrWqr+/QAASNu4h+JWtkaJS1MAAGBalQ1KdUHwAwAgPZUNSkW9fbJo5UG9sT4CSFMV9jGV7R6giniuDwAA2apsjdJAUVIsbaZQJKyPANJUpX1M5YNSFVRphQMAoEwIShkpapsp1FPe6yPbAfLQ7/e1sLCg1dVVtVot+tZLUd77mFkiKJVAlVY4AMhDv9/f8LSGlZUVzc/PSxJhCWNVvsPJKiEooew2X0betSv6l3Uaaet0OlpZWTlufLvd1vLycvYFQqHUssPJKuJgAgDbs7q6OtH4ouFEOT8EJQCZqdNl5Dp8xzJptVoja5RarVYOpUGZ0I9STdAHE4A66/V6ajabG8Y1m031er2cSpTMYN+9b180sC/PHjVKADJX5VoWuvMopkGDbe56S09V13WCUsWx0wbKh+00Hd1ut3TBqE6Xq4uKoASgEopyIOHAhrqp+gk5Qani2GkD5VH1Aw62j3UgPwQloCQ4aI5W1HCR9/yBrFT9hJygVBNVW3GRnqru7Mqg6gccoIwISiilaQ4kZTsIFbXGpCgIF0AxVHXbox+lgqKvjPIr229Ify3Z6/f76nQ6ajQa6nQ66vf7kqIDTlUPOkDZUKOEUpmmdqWsNTPUmCRTtuXCQ1q3r0rbQpW+S1URlAqmrAdzrCvrb1iXQFaU77ewsHAsJA2sra1pYWGBoAQUCEEJpTLNwbzsQaBs5cV4ZX9Iax7KehIySpW+S9URlAqm7AdzlP83LFt5kyragYmHtCItea/bVUNQQilNswNg54Ei6PV6G9ooSeV4SGueyn4SMqxK36XqCEoFxUZTfvyGxVK0AxMPacWsFa3WtCoISkAAOxmkrYwPaS2CKm2TVfouVUU/SgBqhT6KshPqJwrpGKzbu3ZFA+v6bFCjBGxC9TUwPfqJQlVQowQAmLlx/UQhXdQkzRY1SsAmRWv0C5QR/UShKqhRAgDMXKg/KPqJQtkQlIAAqq+B7ev1emo2mxvG0U8UyoigVAPceQIga91uV4uLi2q32zIztdttLS4u5t6Qe/fu9cvq2L46LUfaKFUcd54AxVbltnD0E4UqMHef+UTn5uZ8aWlp5tPF5DqdzsjnSbXbbS0vL898fmns9Kt8IEkDy6tc+L2ysbnbj127on9Z7pOp6nI0swPuPjfqPWqUKo47T4Bior8uoBwIShnq9/uZP9cpqyeUp7HT50AyGZYXEEa3H7NRx+VIUMpIXm2FeEI5UEx1POBUFb9htdFGKSNZtxUalmVNFm2U8sfyKhd+r/Lb/BvmcfUA06GNUgHk2VaIO0+A4iIgldeoy90HDx7U6ip3Gm+lTCcIBKWMZNVWKG9prPRl2JCKhOUF5OeGG27QkSNrkq6Kx1x07Bl3BKVyKmWHk2XsQJFeagGg/IY7Whz03r9rVzTs3SvdfvsjRv4ddxpHBstv375oKEPHlaWrUSprB4qDsnHdGgCq6+STP64jRw5L2h2PiWqWWq2LcyoRplW6xtx5NooGANRT0o4WzzvvoK6//jodPfroeMxeNRoNXX75jZwYDylaG6VxjblLd+ltvfryKq1fA6ZaEwCQv2uu2anLL79RO3bsl7RX7fbFhKSSK92lt7o0ikY1Fe0sapa4JRpVNkm/V91uV5deOvjccnqFKrEy7QNLV6N06qmfUKOxT9H1392SrlKjsY9G0TNSxobyyN+g7eDKyorc/VjbQdYf1NWgoTeSK2rD7tK1URr0U3HttTslSTt27Nc555yja67Zmcr86mRzQ3kpujNvcXFx5jUDVa5ZGaWqD5IcoO0ggGnleVyoVBulvXuja8CD2zEPH76QkDQjCwsLG0KSpGP9fwDjjOtQtahniQBmZ5qrEUXvMqB0bZSQnix6D6/rg1ur/lwv2g4C9VXWbnuSKl2N0sA013+LllaLInRQ42CHrYzqULXR2KdTT/1EYc8SUS2sX/mZ9mrEqI47i3QySY0Sjun1eiPbKM2yoXzVa1a2UtXvO6pD1VNP/X7t3LlT116bc+EApCrPZ5lmoVZBqa6XfZKi93BMI/TwZbYzpIn9ev5mdem9qL9ZaS+9IR3dblfLy8s6evSolpeXUwtJRataBWaJbjZQJ1V/lmnpugeYBc44gHSwbWXbzQbWse7lq+wdzlaqewAAKDK62UAdZXU1Ig+1aqM0wBkHMFtVbCey3e9Q9YatRVXmdQ3FRo0SAMwQ3WwA1VLLGiUAs1Wlbh+mrR3LopsNANmhRgmFlvTuIe4yQlF0u10tLi6q3W7LzNRut2nIDZSZu898uOCCCxyY1p49e7zZbLqkY0Oz2fQ9e/Zs63NFtGfPHm+3225m3m63S1Hmuti1KxrqjnUUdSBpyQOZhqCEwmq32xvCz2Bot9vb+lzRlDng1QFBiXUU6UgavrMM6eOCUi37UUI5NBoNjVo/zUxHjx6d+HNF0+l04t5sr4rHXCRJarfbWl5ezqtYSFHZ2nCtr6MbsY5iu5L2M5Z1f2T0o4RSSnr3UFnvMuI2chQd6yhmLWk/Y0Xqj4yghMJK2i1+WbvPP/nkjyuqTdodD1dJuqrwAQ+TGzzZft++aCjLk+7LehKC4koavosU0glKKKykdw+V9S6jc845R43Gxk2w0WgUPuChPsp6EoLiKuWVglDjpWkGGnMDyezZs8d37Ljapau4o6gGythAnLveMEtFvZtZNOYGiqtsDXyxffzWQPIH6Gb5oN1xjblrF5TK/oRjAAAwW9z1FhvcbriysiJ318rKiubn5+nFeUboHRsAUDW1CkpFut1wFooUTAihAIAqqlVQKtLthtMqWjCpWggtmiKFYmASZekKIQssi3KqVVAq1O2GUypaMKlSCC2aooXiSRDw1rEsgJIK3Q43zVDU7gGq9NwiMxv5fDMzy6U8ZX3eWhmUddkWYXsryu34RVgWWRsseykaivJb5IFlUXwa0z1ArWqUytox4ShFqx2jY7r0lLW2rmi1nnliWQDlVbvuAaoizQcGbrevF7peSEdZH0ya58OKB+vwvn3Rv7t2Rf/m1X9RWR/cPAv0HbWOZVFcdA9QQUWsHet2u1peXtbRo0e1vLx8rCw0YIxst41KWWvrilbrmSeWBVBioWty0wxFbaM0qbpdR07rOnrdluMo07ZRKeNjJIrQLqco614RlgWAMNFGCXko6xPT0zBtG5VQbV2RFbHWMy8sC6C8aKM0QtHaN2Rt83X07bY9qvtyHFbnNioAiok2U+vGtVE6MevCoFw2Nxof9OEjacuwNNj42BijtiijGmTTRgUAio1LbyPs3RsNu3ZFw+B1XQx/X25rno28G2TT2SGAAZpFTIagFNDv97V//37t27e31geWWfThU7egOUqebVTK3LM3kAQnAkhVqJX3NEPZ73rjDpV1Ze0VGuum/Q3LeMcd6oP99fYV5a7QIhB3vU2mzJebZn1mlfclI0xvmlpBaqNQdGXeX6McuOtthLLeoZRWb930uF1u0/TsXdZewVEfZd1fo1jG3fVGUBqhrAeHspYb6ZomQHMQQtGx38Ms8AiTCZX1clNZH56KdE3TkJxHb6Doyrq/RnkQlEYoay+6HNQQst2evYtwEOLW5XrYbvvKsu6vx2GdL5hQK+9phrLf9VZW3P2BNOR519uePXt8x46rXbqKO+4qjH3XRtyNlj2NueuNNkoVQ8NrVMV55x3U9ddfp6NHHx2P2atGo6HLL7+RdbpiaGcU4bFP+aGNUo2k9fBUqoKRtRtuuOG4BuNHjx7N5LZvOjDMFu0rUWQEJQCFdPvtj5B0kaS98XCRpItSP3jSd9Tx0j5RSqt9ZdlO8PbulS65pK8dO/ZL2qvl5Y4uuaS+611REJRSUpUzUp4JhLzkdXMCHRhmrwg3DRTBIKQfOXJYkgjpRRFqvDTNUPfG3FVqmDhoVChFA40MkZUst6Ph9drMRj7yxcxmPt+8jdqeh8dluf3P8qaBsu63eGRUfjSmMfeJmSezGhh3Rlq2RqiDRoSDWiQaFSIrg20l65sTWq3WyIbFdLORrm63W7r946zRVquYCEopYGVHHWQRntM+eG6+y2j3bunUUz+hZvM+x/VkXqXLQKO+96c/LZ1//sZxUvT7lu1EKcsTvFneaUxILybaKKVgfaW+Kh42jy+fvXvLs5PEaLQvS2bnzp2V68AQ6Zh1w3/aahUT/SilYP3ZWh+Kx1w0k4fTAtOY1dl1Fft6KVuNyayM+t51XRbbkUb/T/SFl49x/Shx6S0Fl17aVav1GF177U5J0o4dV6vVOkfd7s6cS1Zt7OBHG3WZRWI5AdNKo5kFbbWKh0tvKdm5cz0UXXjhhRteA2U2uAy7a1c0VOGybBW+w3aM+t55LouyXR6etguLqnQjU3XUKKWAO8WyRY3JeKyPQDp6vV7czGLyhv/rTTSivx20b5KUSY0Sl/iSIygB2BYCF2alrCc703RhkWc3MnmHtLKhMTcqoyw7VxQLZ9b5q+INAltpNBoadfw1s+OecThrPIT4eDTmBoAROLMuhjpeHs6zzyT6+psMjblRGXVtkIvt47luyEuefSbl9RzFsiIoYVu4WwNVwJl1sdTpZKfb7ebWsSkdW06GNkqY2ObLFZLoUBOlRFsN1BVt8zYa10aJGiVMjMsV9bO5f5uq1ChyZo266na7Wl5e1tGjR7W8vFzrkLQVghImxuWKepv1863ylOflj7SUrdNGoOi49IaJcbmiPkbdtr1//34dOfLw4z7L718MdbpzDJgVLr1hprhcUW9HjhweOb7ONYpFqMUZlGHfvmjYTpmqckkVmCX6UcLEpumNFuUyqn+bTudijahQ5NbikqNPKWA0apSwLWVoCMjZcTqoUVw3i1qcWZn2YcVZ3aQx6XZZhNo61Bs1Sqgkzo5na/iAS41iNWVxk0ZRt0vadWEcGnOjkmhwjixV4UCbxTYzyTyyfP5bFX4/TIfG3KgdujAAJpPFJdWibZdFunSK4iIoYduS7FTyaifEs4yQpSo8eiOLPqUm2S6nbXMFzApBCanJs2NCGhwDk5vkJo3t1L4Ubbvcu1e65JK+duzYL2mvlpc7uuQSbvookiLU8hGUplTHO6uSVlfn+aiTKva4DBRZkn3hdrbLNGuSBidzg77BytzL/FaKEDjKisbcU6jrw2GTNrJsNBoatX6ZmY4ePZpa+YAs1e3hoqO2/4MHD2p19T6l2xfW6aaPaRqs59HYPcvG/NL4xtx0DzCFcTUmRd45TGtUJ4SjtFqtkTsh2gmhKop6u3vWbrjhBh05Ur59YdEal6dhc+DgDr/JEZSmUIeNbBq9Xm9kjVuW7RHYKSBNdTxZGnWi1Gg8YuRni74v5GRuvDxDVtIT8izQRmkKdb+zaqu2A2VqJ8T1eySxuR3OqIOsVPyAMGtl3RcWrXF5Grh7cAbcfebDBRdc4HWwZ88ebzabLunY0Gw2fc+ePXkXrfZ27YoGKRoGr7f6PKptz5493m633cy83W5PtK2O2t7NbMPrwdBut9P7EgVU5n3hNOtEmUyzj6vD/lHSkgcyDUFpQptXmLpsZGWTNChNGqhQXtMezNvt9shQtDkslSUgzBr7wuqqw35xXFDirrcJFeF6KZLb6vfK+s4KZGfz3Wi33nqrvv71rx/3uaR3OIXu4hxMo2x3vdXtbj1gHO56m4FxjdoIT+VVpAaDmJ1Rd6OFJG1PFGr4W8ZbyblbD0iOxtyoNBou1tOou9FCkjY4rlLD3zw7gwXKhqCU0Kg7ByQeqFgVBKpqSVpLNEnQKdNdnFuZtmuTPJ9IUMenISBfBCWgQjiIREK1RGedddZUQWeSZ6EV2TS38+f5DMc8540aC7Xynmao8l1vo9ThjgAUX5lv0Z41lsV40yyf0N1/WXSJMMm8uQsPkxDdA6SLoIQimMUBrEoHlyp9lzRsd/mE+o4ys5RLnHzeBGVMiqA0I1nteNnBYzumPYBxcEESZahRyrOMIezXi42gNANZHUQ4WGWvKjuwaQ8ORTy4oHjy3EclnXeetV7TlBv5qU1QSvMSWFYHkVnMp6yXAvMod5V2YNN+l6IdXOqqDME9zzImmXfRQn/RyoPjEZRmIKuDyCzmQ1AK27yTPeussyq1A5vmAMbOPH9VCu55Ktpy5CSk+CoflHbtcj/33K+4FH2jHTuu9nPP/cpM51GGGqWyPrcsq3KP2nmGhjruwIp2cKkjwursFKlmjt+1+MYFpUr0o3Tw4EFdf/11x14fOXJY119/3Uz71siqV94q9f5bNGn01lwlVepQsaym7QgS69Lo82q7/ZSxXy+5UIKaZsi6Rmk9rV8VD+mk9bLc9VaWmqTN0i53qPp780AtCvJCzUNxTVvjWqQaLhxPVb/0tn4A3BiU6nj5xJ2gFBI6CJ111lnswEqkygccLn8WFyG22ioflFiBkQQHoeLbKizX4TeschAsMxpkV9u4oFSJNkpc/0US07bB4Tlq+avDU++r8jy5qpnm+XgouVCCmmbIo3uAurbrQTaKWJNRpZqHpHc+claPLIzatoq4D8DsqOo1SlK5z8J2744GFNd6TcZV8bCxJiPr37CuT1HnrH48aj2nF9q2JHFXaF2FEtQ0Q5keYVKEvoeozSp+7chWNwxk/RtWtV0ebZS2rwjLpgr7sqpuWxhPdahRKqNBLcS+fdFQ15qlMtSOnHzyxxXVJO2Oh6hm6eSTP67zzjt47Dc85ZT9Ou+8g1PNK0mtQF3726Gvp7Bx7bfKWtOUR7nrum2FjPoN0vhdCr2OhhLUNEOZapTcozOxHTuudumqTGszsuhRfJy8vvdmZTiDO/fcr3ijse/YbyVd5Y3GPr/nPa8bOX67yzJprUAZltmkil6rmKdQm5nhcaPWh+F1aKt1ahpp1MznVUNW521r8zHhhS984XG/wUknneQnn3zyTH+XItSGqurdA0xj/Qdav6SS/ZOw6zXvzcrSQHdUsJx1Z6dJd9JF2LHMUtW+zyyNWjajDlah7eiEE05I/cCfRlDKK7AUad84C0m3rVHfO2knvdP+LkUIpwSlMfL8gWZ9kJ3E4IA/XBMyCABZK8JGktTmA8CsOzudJDQWpUZwFsq0DmRtq9qicWFp4wFyNuvoOLNso5TnCVQdt63QMWGSdW+7inCyPC4o1b6NUp7Xo9fncVE8ZDfvI0cOb1Gm7JSpH6y9e6NhYP1uq42/4Xbvwprkrq5ut6sLL7xQu3btLt2dnpuVqV1I1m0JJ1kG7n5c+612uz3ys0W/UzCvOxx375YuvbSrI0culLRbnc6yLr20+ttW6JiQ1DS/S+HvZg0lqGkGapTKNO/sa7NGKWv7lFlfMko6vSLcrTlLk24LeX7fotzhmHR5hdrXFX0by+tybF23rdAxYXNtD22UahiU8vyBijHvalyHz9OsQ16S6VVtZz7ptpBnNx5ZL/OkbZRCyyvvm0ZGSbrs8jyBKvs2NTBNG6Vms+kvfOELt7yRYBa/S94nywSlLeT5A9V13piNquzM3YsfEPOcd5K73rbafou0rhSpLCFlKGNSk9z1VtdjwrigZNH7szU3N+dLS0szny6AjQZtZYbbTVXZ4Pvu2xf9u2tX9G+W379Iy3ySshSh3EX4/YBRzOyAu8+Neu/ErAsDYHbqdoAZfN8iHPTLhmUFbA9BCQAmUITAsblmpizBkaCLMiIoASgdDrAAskJQAoCSKXvNTNnKi3qrfYeTAFAGdX1oNpA3apQAoKSomQHSR1ACgAIra8NtoCq49AYAABBAjRJQQNQaYKDsDbeBsqNGCQAAIIAaJaBAaI+CENYBIB/UKAEAAARQowQUCO1RAKBYqFECAAAIoEYJKCBqkgCgGKhRAgAACCAoAQAABBCUAAAAAghKAAAAAQQlAACAAIISAABAAEEJAAAggKAEAAAQQFACAAAIICgBAAAEEJQAAAACCEoAAAABBCUAAIAAghIAAEAAQQkAACCAoAQAABBAUAIAAAggKAEAAAQQlAAAAAIISgAAAAEEJQAAgABz99lP1OwmSSsznzAAAMDstd397qPeSCUoAQAAVAGX3gAAAAIISgAAAAEEJQAzZ2a3pjTdM83sRUOv72lmf5nGvABAoo0SgBSY2a3ufloK0+1I+qC7P3DW0waAUahRApAJMzvfzPab2WfM7Aozu0s8/n5m9lEz+xcz+5SZ3dfMTjOzj8WvP2tmPxVP5rWS7mtmnzaz3zezjpl9Lp7OKWb21vjz/2xmF8XjLzaz95rZX5vZF83sdfH4E8zsbWb2ufhvfjmP5QKg2E7MuwAAauNySS92931m9ipJL5f0PyT1Jb3W3a8ws1MUncDdLumn3f2Qmd1N0n4z+ytJvy7pge5+vnSshmngFyXJ3R9kZudK+hszu3/83vmSfkjSEUnXmdmbJN1D0tmD2ikzOzOtLw6gvKhRApA6M7uzpDPdfV886jJJjzaz0xWFlSskyd0Pu/uaJJP0GjP7jKSPSjpb0s4tZvMoSW+Pp3Otor7cBkHpY+7+LXc/LOkLktqS/l3SfczsTWb2WEmHZvR1AVQIQQlAniwwvivp7pIuiGuPDko6ZZvTkqKapIHvSjrR3W+W9GBJexXVRv1pgvICqBmCEoDUufu3JN1sZv8lHvUsSfvc/ZCk/zCzJ0uSme0ws6akO0v6qrvfEbc1asd/d4uk0wOz+XtFAUvxJbeWpOtCZYov6TXc/T2SXibpIVN8RQAVRRslAGlomtl/DL1+g6TnSHpzHIT+XdJz4/eeJektcbulOyQ9VVG7pQ+Y2ZKkT0u6VpLc/etm9o9xA+4PS/rjoXn8n3j6n5X0HUkXu/sRs2BF09mS3mpmgxPG35jmCwOoJroHAAAACODSGwAAQABBCQAAIICgBAAAEEBQAgAACCAoAQAABBCUAAAAAghKAAAAAQQlAACAgP8PpXOIbOZLjJgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "print(my_pred.shape)\n",
    "\n",
    "myListMonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
    "myEx = list(range(1,17160))\n",
    "\n",
    "print(len(myEx))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(myEx[:100], y_test[:100],  color='black')\n",
    "plt.scatter(myEx[:100], my_pred[:100], color='blue', marker='+')\n",
    "plt.xlabel('Locations')\n",
    "plt.ylabel('Y (Precipitation in mm)')\n",
    "plt.title('Plotting predicted y vs correct y for the first 100 locations')\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**: We thus see that the model predicts the values correctly for those locations who have a lower precipitation value. As the value increase and as there are more outliers, the model somewhat fails to predict those values correctly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common FAQs for this model:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What are the features of the model? Why?** <br>\n",
    "The features of the model are each of the months of January, February, March, April, May, June, July, August, Sepetember, October, November, and December. The reason for choosing these features is because the trends in annual precipitation vary vastly with respect to monthly temperature depending on which month of the year it is. <br>\n",
    "A small practical example of this could be that these days we experience more fluctuations in precipitation in some months, when all of the months have almost same changes in temperature due to global warming\n",
    "\n",
    "**How was the data cleaned?**\n",
    "We checked if there are any null values, and while there were not any, we did spot some 0 values, which means the data was already cleaned before usage"
   ]
  },
  {
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   "source": [
    "## Conclusion:\n",
    "\n",
    "Mean Annual Precipitations is influenced by the mean monthly temperatures in the months of February, April, July, August, and December. Therefore, it might be more probable to experience sudden, unexpected or excessive rainfall in these months over a year. Lastly, there is a relationship between the global mean monthly temperatures and annual precipitation based on the results of our model. In conclusion, our findings would be extremely helpful in deciding the plan of action taken by humans in order to protect from the dangers of climate change.\n"
   ]
  }
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